Condition Monitoring and Diagnostics for Internal Combustion Engines … · 2020. 11. 3. ·...

QUEENSLAND UNIVERSITY OF TECHNOLOGY

Condition Monitoring and Diagnosticsfor Internal Combustion EnginesUsing In-cylinder Pressure and

Acoustic Emission

Mohammad JafariBMech, MSc

Submitted in fulfilment of the requirement for the degree of Doctor of Philosophy

School of Mechanical, Medical and Process Engineering

Science and Engineering Faculty

Queensland University of Technology

2020

There are no facts, only interpretations.

–Friedrich Nietzsche

i

Statement of original authorship

The work contained in this thesis has not been previously submitted to meet require-ments for an award at this or any other higher education institution. To the best of myknowledge and belief, the thesis contains no material previously published or writtenby another person except where due reference is made.

Date: 21 September, 2020

Signature:

M. Jafari (2020) PhD Thesis - Condition Monitoring and Diagnostics of Internal Combustion Engines

QUT Verified Signature

[This page is intentionally left blank]

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Abstract

Internal combustion (IC) engines are the main source of air pollution in urban areasand significantly contribute to CO2 production, which is one of main greenhouse gasemissions. Early diagnosis of the engine faults and monitoring engine health arecritical to maintaining the minimum level of CO2 and maximum efficiency of an ICengine. This dissertation includes four studies that use advanced analysis techniquesto increase knowledge of the IC engine and to introduce novel methods for monitoringand diagnostic purposes. These four studies are

1. A comprehensive study to investigate the relationship between in-cylinder pres-sure, acoustic emission and other engine variables;

2. In-cylinder pressure reconstruction using acoustic emission;

3. Knock detection and classification using in-cylinder pressure;

4. Misfire detection using acoustic emission.

These studies can help to improve understanding of IC engines, with the aim toprovide information useful in the pursuit of more efficient and reliable engines. Usingbiofuel, which can be considered as a low CO2 intensity fuel, is another possible wayto offset fossil fuels and decrease the total CO2 production. Hence, some countries areusing 5 to 20 percent of biofuel blend with fossil fuel, and also have passed legislationto use higher percentages of biofuel blends in the near future. Hence, this studyconsidered biofuels in addition to fossil fuels.

This thesis will primarily investigate the in-cylinder pressure signal and structureborne acoustic emission (AE) to monitor the engine. The in-cylinder pressure signal isone of the best measures to monitor the performance of an engine and its combustion.This sensor is used to find important parameters such as indicated mean effective pres-sure (IMEP) and heat release rate (HRR). Also, these sensors can be used to diagnosecommon engine faults such as knock or misfire. However, in-cylinder sensors havenot been used in commercial vehicles due to their high price. AE sensors on the other


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hand, are relatively cheap and can be mounted on the external engine structure. Thesesensors can capture high frequency vibration generated by combustion. Therefore,this dissertation will study the use of AE sensors as an alternative to the in-cylinderpressure sensor.

Initially, a comprehensive study is conducted to understand the relationship ofa wide range of parameters including fuel properties, engine performance parame-ters, regulated and unregulated exhaust emissions, in-cylinder pressure parameters andacoustic emission indicators. Six different blends of diesel and biodiesel were usedin this study. Principal component analysis (PCA) with hierarchical clustering wasutilised to reveal the relationship between all these parameters. The study showedthat AE indicators have a strong correlation with engine parameters as well as in-cylinder peak pressure and IMEP regardless of the fuel type, engine speed, and load.Hence, the AE sensor is capable of acquiring a signal that can be utilised to monitor theengine performance and to reconstruct the in-cylinder pressure signal. Furthermore, itis shown that biodiesel can increase the reactivity of soot particles and increase brakespecific NOx while decreasing particle mass and particle number in the accumulationmode.

The occurrence of knocking is a major problem in spark ignition (SI) engines thatlimits their efficiency. To avoid knock, car manufacturers limit the compression ratioand volumetric efficiency of the engine. However, the engine efficiency can increase byallowing the engine to run on some degree of knock for a certain period of time whilestill avoiding damage to the engine. In this thesis, a new technique is proposed basedon in-cylinder pressure to intensify and classify knocking events that can be providedas a feedback to the engine control unit for controlling knock. A k-means clusteringmethod is utilised to group knocking events based on intensity. Then, this is employedas a reference pattern for classifying the combustion intensity of each engine cycle bya k-nearest neighbour analysis.

The importance of the in-cylinder pressure transducer is well known to revealinformation about combustion and exhaust pollution formation, as well as its capabilityto classify knock. Since these sensors are expensive, they are not used commerciallyfor engine health monitoring. Hence, this study will investigate the reconstruction ofthe in-cylinder pressure trace using an AE sensor. Although AE indicators show astrong correlation with in-cylinder pressure parameters, the AE sensor acquires dataat frequencies between 40 kHz to 120 kHz, while the pressure transducer frequency isbelow 20 kHz. Hence, the reconstruction is done in the crank angle domain by meansof the Hilbert transform of AE. Complex cepstrum signal processing analysis with a


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neural network is used to generate a reconstruction regime. Furthermore, the recon-structed signals are used to determine some of the important in-cylinder parameterssuch as IMEP, peak pressure and pressure rise rate.

While the reconstruction of the pressure signal is possible using the AE signal, themethod requires training. Hence, the possibility of using the AE signal is investigatedfor diagnosis of misfire without having knowledge of the pressure transducer. Misfireis another important engine malfunction that mostly occurs in compression ignitionengines. Misfire can both decrease the thermal efficiency of the engine and increaseexhaust pollution. This study shows that the AE sensor is capable of detecting misfirein a modern diesel engine.



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Keywords

Internal combustion engine, biofuel, acoustic emission, in-cylinder pressure, knock,misfire, pressure reconstruction, multi-variate analysis, principal component analysis,k-means clustering, k-nearest neighbours, ceptrum analysis, neural network, particlenumber, particle mass, NOx, particle morphology.



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Abbreviations

Abbreviations

AE Acoustic emission

B Biodiesel

CA Crank angle

CO2 Brake specific CO2

CI Compression Ignition

D Diesel

DAQ Data acquisition

DFT Discrete Fourier transform

E Ethanol

GHG Greenhouse gas

HRR Heat release rate

HT Hilbert transform

IC Internal Combustion

IE Integration of signal envelope

IgnD Ignition delay

IMEP Indicated mean effective pressure

IW Indicated work

kNN k-nearest neighbours

ME Maximum of signal envelope

MPRF Maximum power of first resonance frequency

MPRR Maximum pressure rise rate

NN Neural network

NOx Brake specific NOx

OEM Original equipment manufacturer


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PCA Principal component analysis

PM Particulate matter

PN Particle number

PP Peak pressure

PPT Peak pressure timing

RE Reconstruction error

RMS Root mean square

SES Squared envelope spectrum

SI Spark Ignition

SOI Start of injection

SOC Start of combustion

SVD Singular value decomposition

T Triacetin

TEM Transmission electron microscopy


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List of publications

1. M. Jafari, P. Verma, T. Bodisco, A. Zare, N. Surawski, P. Borghesani, S. Ste-vanovic, Y. Guo, J. Alroe, C. Osuagwu, A. Milic, B. Miljevic, Z. Ristovski, R.Brown, 2019, Multivariate analysis of performance and emission parameters in

a diesel engine using biodiesel and oxygenated additive, Energy Conversion andManagement. 201, 112183.

2. M. Jafari, P. Verma, A. Zare, P. Borghesani, T. Bodisco, Z. Ristovski, R. Brown,In-cylinder pressure reconstruction by engine acoustic emission, MechanicalSystems and Signal Processing (Under review)

3. M. Jafari, P. Weber, O. Toedter, T. Koch, R. Brown, Knock detection and clas-

sification using K-means clustering and k-nearest neighbours, Applied ThermalEngineering (Under review)

4. M. Jafari, P. Borghesani, P. Verma, A. Eslaminejad, Z. Ristovski, R. Brown,2018. Detection of Misfire in a Six-Cylinder Diesel Engine Using Acoustic

Emission Signals. Published in the Proceedings of ASME International Me-chanical Engineering Congress and Exposition (Vol. 52163). American Societyof Mechanical Engineers.

Other journal articles published and prepared during candidature:

1. P. Verma, M. Jafari, SMA Rahman, E. Pickering, S. Stevanovic, A. Dowell, R.Brown, Z. Ristovski, 2020. The impact of chemical composition of oxygenated

fuels on morphology and nanostructure of soot particles. Fuel, 259, 116167.

2. F. Lodi, M. Jafari, R. Brown, T. Bodisco, 2020. Statistical analysis of the results

obtained by thermodynamic methods for the determination of TDC offset in an

internal combustion engine. (No. 2020-01-1350). SAE Technical Paper.

3. A. Zare, T. Bodisco, P. Verma, M. Jafari, M. Babaie, L. Yang, M.M Rahman,


xii

A. Banks, Z. Ristovski, R. Brown, S. Stevanovic, 2020. Emissions and perfor-

mance with diesel an 1 d waste lubricating oil: A fundamental study into cold

start operation with a special focus on particle number size distribution, EnergyConversion and Management, 209, 112604.

4. Y. Guo, Z. Ristovski, E. Graham, S. Stevanovic, P. Verma, M. Jafari, R. Brown,2020. The correlation between diesel soot chemical structure and reactivity.Carbon, 161, 736-749.

5. F. Lodi, A. Zare, P. Arora, S. Stevanovic, M. Jafari, Z. Ristovski, R. Brown,T. Bodisco, 2020. Combustion Analysis of a Diesel Engine during Warm up at

Different Coolant and Lubricating Oil Temperatures. Energies, 13-15, 3931.

6. F. Lodi, A. Zare, P. Arora, S. Stevanovic, M. Jafari, Z. Ristovski, R. Brown, T.Bodisco, 2020. Engine Performance and Emissions Analysis in a Cold, Interme-

diate and Hot Start Diesel Engine. Applied Sciences, 10-11, 3839.

7. N. Sarvestani, M. Tabasizadeh, M. Abbaspourfard, H. Nayebzadeh, H. Karimi-Maleh, T. Van, M. Jafari, Z. Ristovski, R. Brown, 2020. Influence of doping Mg

cation in Fe3O4 lattice on its oxygen storage capacity to use as a catalyst for

reducing emissions of a compression ignition engine. Fuel, 272, 117728.

8. N. Sarvestani, M. Tabasizadeh, M. Abbaspourfard, H. Nayebzadeh, H. Karimi-Maleh, T. Van, M. Jafari, Z. Ristovski, R. Brown, 2020. Synthesize of magnetite

Mg-Fe mixed metal oxide nanocatalyst by urea-nitrate combustion method with

optimal fuel ratio for reduction of emissions in diesel engines. Journal of Alloysand Compounds, 155627.

9. P. Verma, M. Jafari, Y. Guo, E. Pickering, S. Stevanovic, T. Bodisco, J. Fer-nando, D. Golberg, P. Brooks, R. Brown, Z. Ristovski, 2019, An experimental

analysis of morphology and nanostructure of soot particles for butanol/diesel

blends at different engine operating modes, Energy and Fuels, 33, pp.5632-5646.

10. T. Van, A. Zare, M. Jafari, T. Bodisco, N. Surawski, P. Verma, Z. Ristovski,R. Brown, 2019. Effect of cold start on engine performance and emissions from

diesel engines using IMO-Compliant distillate fuels. Environmental Pollution,255, 113260.

11. P. Verma, E. Pickering, M. Jafari, Y. Guo, S. Stevanovic, J. Fernando, D. Gol-berg, P. Brooks, R. Brown, Z. Ristovski, 2019. Influence of fuel-oxygen content

on morphology and nanostructure of soot particles, Combustion and Flame, 205,pp.206-219


xiii

12. A. Vaughan, S. Stevanovic, M. Jafari, M. Rahman, R. Bowman, K. Fong, Z.Ristovski, and I. Yan. 2019. The effect of diesel emission exposure on primary

human bronchial epithelial cells from a COPD cohort: N-acetylcysteine as a

potential protective intervention. Environmental research 170: 194-202.

13. A. Vaughan, S. Stevanovic, M. Jafari, R. Bowman, K. Fong, Z. Ristovski, andI. Yang. 2019. Primary human bronchial epithelial cell responses to diesel and

biodiesel emissions at an air-liquid interface. Toxicology in Vitro 57: 67-75.

14. S. Rahman, T. Van, F. Hossain, M. Jafari, Ashley Dowell, M. A. Islam, M. N.Nabi, Z. Ristovski, R. Brown 2019. Fuel properties and emission characteristics

of essential oil blends in a compression ignition engine. Fuel 238 : 440-453.

15. Y. Guo, S. Stevanovic, P. Verma, M. Jafari, N. Jabbour, R. Brown, L. Cravi-gan Z. Ristovski. 2019. An experimental study of the role of biodiesel on the

performance of diesel particulate filters. Fuel 247 (2019): 67-76.

16. L. Gannaway, P. Verma, M. Jafari, Z. Ristovski, 2019. Comparison of diesel

engine performance and emissions using diesel and alternative fuels. The Aus-tralian Mine Ventilation Conference. 26-28 August 2019, Perth, Australia

17. S. Rahman, T. Mahila, A. Ahmad, M. Nabi, M. Jafari, A. Dowell, M. Islam,A. Marchese, J. Tryner, P. Brooks, T. Bodisco, S. Stevanovic, T. Rainey, Z. D.Ristovski, R. Brown, 2019. Effect of Oxygenated Functional Groups in Essential

Oils on Diesel Engine Performance, Emissions, and Combustion Characteris-

tics. Energy and Fuels, 33(10), 9828-9834.

18. M. Jafari, P. Verma, A. Zare, T.A. Bodisco, Z.D. Ristovski, R.J. Brown, 2018.Investigation of Diesel Engine Combustion Instability using Dynamical Systems

Approach. In Proceedings of Australian Fluid Mechanics Conference (AFMC)Combustion section. 10-13 December 2018, Adelaide, Australia. AustralianFluid Mechanics Society.

19. P. Verma, M. Jafari, E. Pickering, Y. Guo, S. Stevanovic, R. Brown, Z. Ris-tovski, 2018. Impact of fuel oxygen on morphology and nanostructure of soot

particles from a diesel engine. In Proceedings of Australian Fluid MechanicsConference (AFMC) / Combustion section. 10-13 December 2018, Adelaide,Australia. Australian Fluid Mechanics Society.

20. P. Verma, A. Zare, M. Jafari, T. Bodisco, T. Rainey, Z. Ristovski, R.J. Brown,2018. Diesel engine performance and emissions with fuels derived from waste

tyres. Scientific reports, 8(1), 2457, pp.1-13.


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21. S. Rahman, M. Nabi, T. Van, K. Suara, M. Jafari, A. Dowell, M. Islam Z. Ris-tovski, R. Brown. 2018. Performance and combustion characteristics analysis

of multi-cylinder CI engine using essential oil blends. Energies 11, no. 4: 738.

22. Guo, Yi, M. Jafari, Svetlana Stevanovic, and Zoran Ristovski. 2017. The

performance of the after-treatment devices on the biodiesel particulate size.CASANZ (Clean Air Society of Australia and New Zealand).

23. A. Vaughan, S. Stevanovic, M. Jafari, B. Miljevic, Z. Ristovski, R. Bowman,K. Fong, and I.Yang. 2017. The effect of diesel emission exposure on intracel-

lular signaling pathways of primary human bronchial epithelial cells. EuropeanRespiratory Journal: PA4458


xv

Acknowledgements

I would like to express my deepest appreciation to my principal supervisor, Pro-fessor Richard Brown, for his continuous kind guidance, support and for what I havelearnt from him. His selfless time and care were sometimes all that kept me going. Ialso wish to extend my appreciation to my kind and supportive supervisor ProfessorZoran Ristovski for his significant contribution to my PhD study. Working with himwas very enjoyable, full of encouragement and hope. I would like to thank Dr. TimothyA. Bodisco, for his invaluable knowledge and kindness. I would also like to thank Dr.Pietro Borghesani for his support and sharing his great knowledge in signal processing.I am also very thankful to Dr. Ali Zare for all of his support and assistance, not onlyas a mentor but also as a wonderful friend.

I would like to thank Mr. Noel Hartnett and Dr. Amir Moghaddam for their kindassistance during experiments in the QUT engine lab. This project could not have beendone without their effort and diligence. I also wish to thank my colleagues from theBiofuel Engine Research Facility (BERF) and International Laboratory of Air Qualityand Health (ILAQH).

I would like to thank the Australian Government for providing an opportunity forme, as an international student, to continue my study at PhD level. The financial sup-port provided by Queensland University of Technology (QUT) through the QUTPRAScholarship and QUT Tuition Fee Scholarship is highly appreciated. Additionally, Ithank the professional editor of my thesis, Ms. Wendy Rayner, for providing proof-reading service.

I would like to thank my parents, Shahla and Jalal, and my sister, Sara, who havealways supported me and believed in me. I would not be where I am today withoutyour love and encouragement. Last, but not least, is my lovely wife Farzaneh. Herselfless love, infinite patience and constant encouragement were a source of strengthfor me throughout the various frustrations of graduate student life. I am glad that wehave made this journey together.



Contents xvii

Contents

Statement of original authorship i

Abstract iii

Keywords vii

Abbreviations ix

List of publications xi

Acknowledgements xv

Table of contents xix

List of figures xxii

List of tables xxiii

1 Introduction 1

1.1 Overview and motivation . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.1 In-cylinder pressure . . . . . . . . . . . . . . . . . . . . . . 2

1.1.2 Acoustic emission . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Biofuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Research significance . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.4 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 3


xviii Contents

1.5 Structure of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Literature review 9

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 In-cylinder pressure application . . . . . . . . . . . . . . . . . . . . 11

2.3 Engine structure-borne vibration and acoustic emission . . . . . . . . 13

2.3.1 Vibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.3.2 Structure-borne acoustic emission . . . . . . . . . . . . . . . 15

2.4 Signal processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.5 Knock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.6 Misfire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.7 Research gap and conclusion . . . . . . . . . . . . . . . . . . . . . . 20

3 Effect of bio-fuels on engine performance and emissions 23

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.2 Materials and methodology . . . . . . . . . . . . . . . . . . . . . . . 29

3.2.1 Observations and parameters . . . . . . . . . . . . . . . . . . 29

3.2.2 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . 32

3.2.3 Data analysis techniques . . . . . . . . . . . . . . . . . . . . 33

3.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

4 Pressure reconstruction using acoustic emission 47

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

4.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.2.1 Data analysis techniques . . . . . . . . . . . . . . . . . . . . 53

4.2.2 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . 56

4.3 Pressure reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . 56


Contents xix


4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

5 Engine knock detection and classification 65

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

5.2 Knock metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

5.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

5.3.1 Source of data . . . . . . . . . . . . . . . . . . . . . . . . . 74

5.3.2 Analysis method . . . . . . . . . . . . . . . . . . . . . . . . 76

5.4 Selection of detection method . . . . . . . . . . . . . . . . . . . . . 77


5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

6 Engine misfire detection 85

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

6.2 Test rigs and instrumentation . . . . . . . . . . . . . . . . . . . . . . 90

6.3 Analysis techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

6.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

7 Conclusions 97

7.1 Results summary and conclusion . . . . . . . . . . . . . . . . . . . . 97

7.2 Recommendations for the future . . . . . . . . . . . . . . . . . . . . 99

Bibliography 121

A Supplementary data for PCA analysis 123

B Further information on knock experiment 129



List of Figures xxi

List of Figures

1.1 Data, analysis method and objectives . . . . . . . . . . . . . . . . . . 4

1.2 A diagram outlining the structure of this PhD research program . . . . 7

2.1 Pressure vs. crank angle diagram-diesel engine . . . . . . . . . . . . 12

3.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.2 Analysis diagram of this study . . . . . . . . . . . . . . . . . . . . . 35

3.3 Eigenvalues associated with principal components on left and cumula-tive variance explained by PCs on right. . . . . . . . . . . . . . . . . 36

3.4 Correlation matrix illustration . . . . . . . . . . . . . . . . . . . . . 37

3.5 Loadings Plot of the first three principal components . . . . . . . . . 38

3.6 Dendrogram of hierarchical clustering based on the first three principalcomponents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.1 Feed-forward neural network applied in this study . . . . . . . . . . . 55

4.2 Sensors setup on the single cylinder engine . . . . . . . . . . . . . . 57

4.3 Pressure and acoustic emission signals . . . . . . . . . . . . . . . . . 57

4.4 The combination of fuel, load and speed used in this study. Circles areused for training and crosses for validation . . . . . . . . . . . . . . . 58

4.5 Original and reconstructed pressure . . . . . . . . . . . . . . . . . . 59

4.6 Error of reconstructed pressures obtained from validation data. . . . . 60

4.7 Reconstructed pressures with errors of 3.31%, 5.65%, and 20.41%. . . 60


xxii List of Figures

4.8 Probability distribution function of PP, PPT, IMEP and MPRR withtheir errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5.1 Engine pressure data set used for knock metrics shown at 1500 rpmfrom E25. Row 1: In-cylinder pressure. Row 2: Apparent heat re-lease rate. Row 3: Band-pass filtered in-cylinder pressure. Row 4:Spectrogram of band-pass filtered in-cylinder pressure. . . . . . . . . 75

5.2 Pearson’s correlation of knock metrics with engine speed and fuel type.Blue colour shows positive correlation and red colour shows negativecorrelation. The size of circle shows the magnitude of correlation. . . 78

5.3 A control block diagram of engine with knock detection and classifi-cation as a feedback . . . . . . . . . . . . . . . . . . . . . . . . . . 79

5.4 k-means clustering of the observation (Reference cycles). Red colourshows high intensity knock, orange colour moderate knock, blue colourslight knock, and green colour no-knock events. . . . . . . . . . . . . 80

5.5 Change in the error of knock detection of kNN by increasing the num-ber of neighbours (k) . . . . . . . . . . . . . . . . . . . . . . . . . . 81

5.6 a) kNN classification of the observation (Validation set) b) kNN classi-fication together with K-means. “A” and “B” shows an observation bykNN and k-means, respectively. Colours show the combustion eventclasses, which are chosen by the corresponding method. . . . . . . . . 82

6.1 Test setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

6.2 Process flow chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

6.3 Raw AE and pressure signals of one cycle . . . . . . . . . . . . . . . 93

6.4 Squared envelope spectrum (SES) 33.33 shaft rotation frequency 99.9combustion frequency . . . . . . . . . . . . . . . . . . . . . . . . . . 94

6.5 Comparison of SES Peaks (LL - Low load, HL – High load) . . . . . 95

6.6 Synchronous average of AE signal Envelope in crank angle domain . 95


List of Tables xxiii

List of Tables

3.1 Fuel properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.2 Parameters used in multivariate analysis . . . . . . . . . . . . . . . . 31

3.3 General interpretation of parameters in each cluster . . . . . . . . . . 40

3.4 The relationship between pairs of clusters . . . . . . . . . . . . . . . 41

4.1 Engine specification . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

5.1 Knock metrics and their definition . . . . . . . . . . . . . . . . . . . 71

6.1 Tested engine specification . . . . . . . . . . . . . . . . . . . . . . . 90

A.1 Engine performance parameters . . . . . . . . . . . . . . . . . . . . 123

A.2 Engine in-cylinder derived parameter . . . . . . . . . . . . . . . . . . 124

A.3 Acoustic emission indicators and regulated emission parameters . . . 125

A.4 Particle number . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

A.5 Particle chemical properties . . . . . . . . . . . . . . . . . . . . . . . 127

A.6 Particle physical properties . . . . . . . . . . . . . . . . . . . . . . . 128


1

Chapter 1

Introduction

1.1 Overview and motivation

Internal combustion (IC) engines have a wide range of applications and are the back-bone of many industries including land and maritime transportation, construction,agriculture, and power generation (Ghoniem, 2011). Their success is due to theirreliability, high power density, and relatively low maintenance cost (Pulkrabek, 2013).On the other hand, IC engines are one of the main sources of pollution in most ur-ban areas and produce a considerable amount of greenhouse gas (GHG) emissions(Fontaras et al., 2017). Electrification of passenger vehicles to replace IC engines hasbeen commercially initiated from the early 2000s; however, this transition to electricpowertrains will take place over a lengthy period. A few countries have set milestonesto reduce and eventually stop the production of internal combustion engines by 2030and 2040 while major economies, such as the United States and China, still do not haveplans to ban IC engines. Furthermore, IC engines will remain the primary power sourcein future heavy-duty power trains. Therefore, the study of IC engine performance andemissions still has great importance.

The exhaust emissions of the IC engine have adverse health effects on humans andconsequences for the environment. Strict restrictions are legislated to reduce pollutantsand GHG emissions (Frey, 2018). Original equipment manufacturers (OEMs) haveutilised different methods to increase engine efficiency and reduce pollutants, such asexhaust after-treatment devices, real-time engine monitoring and controlling to meetregulations. Since a number of parameters are relevant, modern engines are equippedwith a wide range of sensors. For example, a 7.3 litre (L) Ford diesel engine producedin 1997 was equipped with 7 sensors and a 6.9 L one in 2015 had 22 more sensors.Engine monitoring in real time can enable the early diagnosis and detection of faultsand real-time management of engine parameters (Delvecchio et al., 2018). It can


2 Chapter 1. Introduction

decrease maintenance expenses and improve efficiency, while real-time changes ofengine parameters can optimise engine emission and fuel economy.

Sensors typically installed on the engines include thermocouples, intake boost andexhaust back pressure sensors, tachometers, and NOx sensors. These sensors provideinformation on general engine performance; however, they barely show the quality ofcombustion in engine cylinders. Having practical information from the combustionprocess can lead to better understanding of the engine, with the aim to pursue moreefficient and reliable engines. Combustion information can be directly acquired fromengine cylinders by using pressure transducers or be indirectly extracted from theengine structure-borne vibration and acoustic emission by utilising acoustic emissionsensors coupled with advanced signal processing methods.

1.1.1 In-cylinder pressure

The in-cylinder pressure transducer is one of the most useful sensors that can be usedfor engine combustion condition monitoring purposes (Payri et al., 2010). The in-cylinder pressure signal can provide a wide range of combustion parameters such asstart of combustion (SOC), heat release rate (HRR), and pressure rise rate. Further-more, the combustion parameters can be utilised to estimate exhaust emissions such asNOx (Guardiola et al., 2017, Lapuerta et al., 1999). However, pressure transducersare not widely used in the engine industry due to their high price (Bertola et al.,2006). Thus, using a lower cost technique and sensor with an acceptable performanceto replace the pressure transducer can benefit the engine manufacturing industry byallowing real-time monitoring of the engine.

1.1.2 Acoustic emission

Structure-borne acoustic emission (AE) has been reported as a significant source of in-formation regarding fault diagnosis and the health monitoring of structures and rotatingmachines (Wang et al., 2016). Acoustic emission is produced by the release of energyin a process that leads to the propagation of elastic waves within a medium. There areseveral studies in engine research that used an AE sensor. Most of these studies arefocused on the utilisation of AE to detect mechanical faults within the engine such aspiston slap, and valve and injector faults (Lowe et al., 2011, 2015). On the other hand,very few studies used AE to monitor engine combustion. Since AE sensors can capturethe elastic wave in the engine structure generated by combustion, an AE sensor is ableto be used as an alternative to an in-cylinder pressure transducer.


1.2. Biofuels 3

1.2 Biofuels

Due to the adverse effects of petroleum fuels on the environment and health, the ParisAgreement of 2015 highlighted the necessary reduction of petroleum fuel consump-tion. Biofuels are potential alternatives to fossil fuels, with similar properties whichcan be directly used in the internal combustion engine (Agarwal, 2007). Biofuelscan reduce some exhaust pollutants such particle mass, and decrease GHG emissions(Panwar et al., 2011). Since biofuels are produced from renewable resources thatconsume CO2 such as sorghum or sugar cane, they can be closer to CO2 neutral.This can help to reduce the total GHG emission from internal combustion engines.Hence, regulatory commissions around the world promote the use of biofuel blendswith fossil fuel to mitigate the effect of CO2 on climate (Niculescu et al., 2019). Forexample, European Union legislation requires their members to increase biofuel usagein the transport sector to at least 10% by 2020 and to 14% by 2030.

1.3 Research significance

In this thesis, the condition-based monitoring and diagnosis of an IC engine fuelledby petroleum and biofuels is investigated using in-cylinder pressure transducers andacoustic emission sensors. This study shows the effect of fuel properties, exhaust emis-sions and engine performance on in-cylinder pressure and acoustic emission derivedindicators using a wide range of fuel blends. Then, it investigates the possibility of in-cylinder pressure reconstruction using acoustic emissions. It addresses the detectionand classification of two important combustion faults, namely knocking and misfire,by using pressure transducers and acoustic emission sensors. This thesis developsdifferent analysis methods to extract useful information from the sensors. It providesa better understanding of the in-cylinder pressure transducer and acoustic emissionapplications in the modern internal combustion engine. The analysis methods, coupledwith the sensors, can provide more reliable tools and information useful in the pursuitof more efficient and reliable engines. This investigation will be a practical source forinternal combustion engine researchers and industries.

1.4 Research Objectives

This research aims to investigate the in-cylinder pressure transducer and acoustic emis-sion sensor data using advanced analytical techniques to extract useful informationwhich will detect engine faults. This information can be practical in developing more



efficient engine control systems that improve the total life and efficiency of engines.The following objectives address this aim:

1. Investigate the relationship between engine performance, exhaust emissions, fueltype, in-cylinder pressure and acoustic emission;

2. Reconstruct in-cylinder pressure using acoustic emission to extract importantin-cylinder information;

3. Detect and classify the knocking event using an in-cylinder pressure transducer;

4. Detect misfire in an engine using an acoustic emission sensor.

To address these objectives, four major experimental campaigns were performed,followed by advanced data analysis, as described as follows and shown in Figure 1.1:

1. Using a variety of fuels on an engine to reveal the relationship between a widerange of engine parameters, fuel properties, in-cylinder pressure data and acous-tic emission indicators using principal component analysis;

2. A fundamental experiment on a single-cylinder engine to study in-cylinder pres-sure reconstruction from acoustic emission using complex cepstrum and neuralnetwork analysis;

3. Running an engine fuelled by three types of fuel on knock to intensify knockingindicators using k-means clustering and k-nearest neighbours;

4. Motored experiment on an engine to simulate misfire and develop a detectiontechnique based on acoustic emission using envelope and order analysis.

In-cylinder pressure Structure-borne

acoustic emission

Fuel, engine, exhaust emission parameters

k-means clustering, k-nearest neighbours

Principal component analysis, hierarchical

clustering

Knock detection and classification

Pressure reconstruction using

acoustic emission

Misfire detection

Complex cepstrum and neural network

Dimension reduction, identification of

dominant relationships

Envelope and order analysis

Figure 1.1: Data, analysis method and objectives


1.5. Structure of the thesis 5

1.5 Structure of the thesis

Initially, a multivariate analysis technique is utilised to assess the relationship betweenforty parameters from engine performance, exhaust emissions, in-cylinder pressuremeasures and acoustic emission indicators. This comprehensive study is done usingsix diesel-biodiesel blends and also triacetin as an oxygenated additive. Since the dataset is large, an analysis technique based on principal component analysis (PCA) andhierarchical clustering is used. This study reveals the correlation between acousticemission and in-cylinder pressure indicators. Furthermore, it shows that some ofthe exhaust emission parameters are correlated with in-cylinder pressure and acousticemissions. The PCA gives justification for further investigation of acoustic emissionsas a diagnostic tool for engine performance and emission parameters in Chapters 4, 5and 6. This study on multi-variate analysis of engine parameters and exhaust emissionsis presented in Chapter 3 and published in Energy Conversion and Management.

Chapter 4 focuses on the feasibility of in-cylinder pressure reconstruction fromacoustic emissions. This is important, since acoustic emission sensors are relativelycheap compared to pressure transducers. Thus, a wide range of information can beextracted from acoustic emission which has traditionally only been provided by in-cylinder pressure. An algorithm based on complex cepstrum analysis with neuralnetworks is developed to reconstruct the pressure trace using acoustic emission. Thisstudy is to be submitted to Mechanical Systems and Signal Processing.

Knock is an important issue in spark ignition engines that limits thermal efficiency.While knock can damage the engine, the engine can run on some level of knock for ashort period of time. This cannot be done without classifying the intensity of knocking.k-means clustering based on the in-cylinder pressure was studied to classify sparkignition knock. Furthermore, the k-nearest-neighbours method is utilised to assessknock identification with the aim of it being used in real time. This is presented inChapter 5 and submitted to Applied Thermal Engineering.

The final study, shown in Chapter 6, assesses the detection of misfire using anacoustic emission sensor in a multi-cylinder diesel engine. The detection of misfire isimportant since it can reduce the general efficiency of an engine. An experiment wasdesigned to run an engine with and without injection of the fuel in the first cylinderto simulate a firing and misfiring event. The acoustic emission signal was acquiredsynchronously with the crank angle signal, in order to have a reference for the trans-formation from time to angular domain (crankshaft degrees). The AE signal was thenprocessed using the squared envelope spectrum to highlight angle-periodic modula-tions in the signal power. This study presented the effectiveness of this combination of



sensor technology and signal processing to detect misfire in a six-cylinder diesel engineconnected to a hydraulic dynamometer. This study is published in the Proceedings ofthe American Society of Mechanical Engineers.

Chapters 3-6 of this thesis represent journal publications (either accepted for pub-lication or under-review), Papers 1-4 respectively, as shown in Figure 1.2. The compi-lation of these papers constitute the main component of this thesis by publication. Thecomprehensive engine performance and emission parameters, including in-cylinderpressure parameters and structure-borne acoustic emission signals is studied usingPCA (Paper 1). This study is published in Energy Conversion and Management.The second paper on reconstruction of in-cylinder pressure using AE is submitted toMechanical Systems and Signal Processing. The in-cylinder pressure, as an importantaspect of engine performance, is used to build a knock diagnosis and classificationtechnique (Paper 3). This study is submitted to Applied Thermal Engineering. AEsignal is utilised to detect misfire in a diesel engine as a non-destructive method (Paper4). This paper is published in the Proceedings of International Mechanical EngineeringCongress and Exposition (the American Society of Mechanical Engineers).


1.5. Structure of the thesis 7

Introduction

Chapter 1

Literature review

Chapter 2

Greater understanding and control of engine

operation through the application of advanced

data analysis

Investigating the correlation between in-cylinder pressure, acoustic emission, other

engine parameters using principal component analysis

Chapter 3 – Paper 1

Pressure reconstruction from acoustic

emission using cepstral analysis and neural

network


Knock detection and classification based on

in-cylinder pressure using k-means

clustering and k-nearest neighbours


Misfire detection using envelope and order

analysis of acoustic emission signal


Conclusion

Chapter 7

Figure 1.2: A diagram outlining the structure of this PhD research program. Papernumbers correspond to the list given on Page ix - List of Publications



9

Chapter 2

Literature review

2.1 Introduction

The thermodynamic analysis of reciprocating engine was fundamental in the devel-opment and increase of the efficiency of current engines. Pressure, volume and tem-perature are principal parameters for combustion analysis (Cengel, 2014). While in-cylinder temperature is difficult to be measured with high resolution, the in-cylinderpressure signal and volume are less challenging to acquire (Heywood, 1988). Volumecan be determined in real-time from the crank angle, crank radius, rod length, cylindersize and compression ratio. In-cylinder pressure can be measured with more compli-cated sensors such as piezoelectric transducers. Most of the in-cylinder parametersrequired for engine design and control are derived from pressure and volume suchas heat release rate, indicated mean effective pressure and thermal efficiency. Fur-thermore, in-cylinder pressure information is one of the best measures to monitor theengine performance and to diagnose faults such as knock or misfire (Mohammadpouret al., 2012).

The measurement of in-cylinder pressure poses a challenging task for originalequipment manufacturers (Leipertz et al., 2010). Pressure measurements need to havea high resolution and high accuracy in order to determine the engine and combustionperformance parameters. Generally, there are two methods to measure the in-cylinderpressure: intrusive method and non-intrusive method (Rogers, 2010). Intrusive meth-ods make use of sensors that can be installed directly in the cylinder or combustionchamber. Non-intrusive methods are based on the measurement of indirect effect ofcombustion on the engine structure or crank shaft.


10 Chapter 2. Literature review

Intrusive sensors must have high operating temperature, high sensitivity, high nat-ural frequency and above all they should withstand intense transient nature of combus-tion (Rogers, 2010). Piezoelectric technology is widely adapted in engines researchand development to measure in-cylinder pressure (Bertola et al., 2006). Piezoelectricpressure transducer incorporates a piezoelectric crystal that generates electric when achanging force is imposed on it. The signal has a linear relationship with the forceor pressure (Bodisco et al., 2013). To install the pressure transducer, a hole should bedrilled through the engine head and the transducer sensing tip is placed in the combus-tion chamber. These sensors are highly accurate, reliable and small size. Hence, theyare utilised where a detailed pressure measurement is required for precise combustionanalysis. Although this type of pressure sensor is ideal for monitoring of the engine’sperformance, they are expensive due to their complexity as a unit and in their assemblyprocedure (Chiatti et al., 2017).

Other alternative sensing technology to piezoelectric are fibre optic sensors andcylinder ion-current measurement (Rogers, 2010). Fibre optic sensors incorporate twomain parts an optical fibre and a flexing metal diaphragm (Poorman et al., 1997). Themeasuring diaphragm deflects as a function of the in-cylinder pressure. The opticalreceiver monitor the light reflection from the diaphragm and detect its deflection.The fibre optic sensor’s output is proportional to the pressure that is applied to thediaphragm. These sensors are relatively low-cost and have a compact size with rea-sonable durability. However, they require fine tuning and do not provide adequatesensitivity and linearity (Ladommatos and Zhao, 2001). Ion current sensing is a wellestablished technology that detects negatively charged ions in the combustion chamberby creating an electrical field inside the chamber (Saitzkoff et al., 1997). Although ioncurrent sensors cannot directly measure the in-cylinder pressure, they can provide somecombustion information such as maximum peak pressure location. This technologyoffers very low price sensors which mainly useful in detection of misfire and knock(Cavina et al., 2011).

An ideal sensor for combustion monitoring needs to be non-perturbing and non-intrusive (Rogers, 2010). The combustion flow field can be affected by any protru-sion in the chamber (Payri et al., 2004). There has been a growing interest in thedevelopment of an alternative technique to indirectly monitor combustion (Delvecchioet al., 2018). These techniques mainly use the engine block vibration and crank shaftfluctuation to extract information from engine cylinders (Dunne and Bennett, 2020).Accelerometer or structure-borne acoustic emission (AE) sensors have been used todetect in-cylinder related events, such as knock, and to monitor non-cylinder pressureevents such as vales and injectors vibration response (Lowe et al., 2011, 2015). Thesesensors can be the alternative to pressure transducers, as a strong and meaningful


2.2. In-cylinder pressure application 11

relation between the engine block vibration and in-cylinder pressure was reported inprevious studies (Antoni et al., 2002b, Delvecchio et al., 2018). These sensors arerelatively low-cost and provide high accuracy.

In this thesis, piezoelectric in-cylinder pressure transduce will be used since theyprovide reliable and accurate pressure signal. AE sensor will be investigated as anon-intrusive method to provide a better understanding of its applications in the mod-ern internal combustion engine. Two important combustion anomalies, knocking andmisfire, will be studied by using pressure transducers and acoustic emission sensors,respectively.

2.2 In-cylinder pressure application

The in-cylinder pressure profile is a significant source of information. The pressuresignal represents the actual states of the engine based on time or crank angle (CA)(Traver, 1999). Figure 2.1 shows pressure/crank angle diagram of a compressionignition engine cycle. The pressure transducers are usually employed to determineinformation about the engine. Standard examples are typical thermodynamic conceptssuch as indicated power, indicated mean effective pressure, heat-release rate, thermalefficiency, pressure rise rate and peak pressure (Heywood, 1988). This informationcan be obtained by using pressure indications with volume and crank angle data.Many researchers have found the in-cylinder pressure useful to determine the start ofcombustion (SOC) and, hence, ignition delay. Three main techniques have been usedto determine the SOC: optical based (Pischinger et al., 1988), in-cylinder pressurebased (Oh et al., 2015, Rothamer and Murphy, 2013) and vibration based (Carlucciet al., 2006a). The pressure-based technique is mostly used in the research due to itsless complex signal analysis. The sudden change in the acquired pressure signal can beconsidered to find the SOC (Rothamer and Murphy, 2013). More common methods areto employ the first, second and third derivatives of pressure signal with respect to CA(Katrasnik et al., 2006). The rate of heat release, which is based on the first derivativeof pressure, can also be used to determine the SOC (Oh et al., 2015). Ignition delaycan be easily determined by calculating the SOC along with the SOI from the injector(Bodisco et al., 2013).

Another application of pressure transducers is to identify knocking events in en-gines (Shen et al., 2019). Knock can cause damage to engine components and reducethe life of the engine. In-cylinder pressure evaluation is the best standard of knockidentification, since it directly shows the severe pressure wave caused by auto-ignition



-360 -300 -200 -100 0 100 200 300 360Crank angle [deg]

Intake valve closes

Exhaust valve closes

Start of injection

Start of combustion

Top dead centre

End of combustion

Exhaust valve opens

Intake valve opens

Figure 2.1: Pressure vs. crank angle diagram-diesel engine.

in the cylinder (Shahlari, 2016). The knock identification is mainly limited to a thresh-old definition based on a knock metric; however, this method can lead to false detectionor misdetection of knock (Angeby et al., 2018, Wang et al., 2017). This problem can be¨ addressed by classifying the intensity of knocking events. The classification of knocknot only improves the accuracy of knock detection, but also increases the overall engineefficiency (Angeby et al., 2018).

Signals filtration is a useful tool to remove noise from lower frequency componentsof pressure trace, and to isolate higher frequency components which indicate the com-bustion chamber resonance. Douglas et al. (1997) employed a low-pass filter usingsimple electrical hardware to reduce the background noise from the pressure signal,and exerted a timing correction to provide an acceptable time lag. They also proposed amoving average filter for the offline analysis of the acquired pressure signal. However,Shi and Sheng (1987) indicated that the use of a filter based on a moving average mayshow some problems, for instance, this technique may not eliminate the noises fromduct resonances and, also, this technique’s smoothing capability is dependent on thesampling interval.

Payri et al. (2005) proposed a method for the analysis of combustion noise basedon the decomposition of pressure signals in three divisions: pseudo-motored, combus-tion and resonance. Their results showed a qualitative relation between combustionpressure and noise. This relation can help to design an optimisation procedure for theinjection strategies. Moreover, they pointed out that combustion and resonance signalsare the function of speed and load, respectively. In another study, Payri et al. (2010)


2.3. Engine structure-borne vibration and acoustic emission 13

utilised a finite impulse response filter to optimise the analysis of pressure signals foronline and offline processing purposes. They determined the cut-off frequency basedon a cut-off harmonic map. They improved their technique by considering the DiscreteFourier Transform representation of the pressure signal (Payri et al., 2011).

According to the literature, the pressure signal can be used for emission control(Beasley et al., 2006), exhaust emission prediction (Traver et al., 1999), combustionchamber temperature estimation (Bodisco et al., 2015), and knock detection (Wanget al., 2017), etc. As stated earlier in this section, the in-cylinder pressure signalis indeed suitable for engine monitoring purposes and fault detection. Although thepressure transducer is the best way to have the cylinder pressure trace, it is expensiveand complex due to the setup process required. Thus, using another technique with anacceptable performance to measure the pressure can improve the real-time monitoringof the engine, and can be broadly used in the CI engine industry.

2.3 Engine structure-borne vibration and acoustic emission

2.3.1 Vibration

Using engine block structure-borne vibration is an alternative to the in-cylinder pres-sure transducer for engine health monitoring. Internal combustion engines inherentlygenerate significant vibration energy that is mostly undesirable, since it can cause fa-tigue in some of the engine parts and increase acoustic noise. On the other hand, thesevibration signals can be a representative quality indicator of the engine performanceand health monitoring (Antoni et al., 2002a). Every physical phenomenon and movingmechanical part in the running engine generates its own unique vibration signal, whichcan be called a vibration signature. In order to have a successful methodology, thevibration information should be broken down into its different contributions, then eachassociated with their respective excitation sources (Antoni et al., 2002a).

Many CI engine sources of vibration are related to phenomena that happen neartop-dead-centre (TDC) of the cylinder (Antoni et al., 2002a, Lowe et al., 2015). Sincethese vibration signatures overlap, the analysis of vibration signals becomes difficult.Thus, some research has been published regarding separation of vibration sourcesin diesel engines. Badawi et al. (2006) separated the frequency of the inlet/exhaustvalve opening/closure, fuel injection, piston slap and combustion using independentcomponent analysis based on angle-frequency analysis. Liu et al. (2008) proposed ablind source separation method to segregate the source of vibration from a four cylinderCI engine that has a faulty piston. They used fifteen accelerometer sensors attached



on the engine block around a cylinder. Using the Blind Least Mean Square algorithmwith a deflation method, they found a reasonable result for the variation of combustionpressure, injection and piston slap, although they suggested a finding of an optimisedlocation of sensors on the block.

Many other researchers have utilised various vibration signal analysis methodsto diagnose CI engine faults. Chandroth et al. (1999) utilised principal components,orthogonal wavelets and domain expertise to train the neural network for engine faultdiagnosis. Of these methods, they found that the orthogonal wavelets approach givesthe best performance in detecting leaking valves and injector faults. Shen et al. (2000)used the rough set theory to identify the diesel engine valve faults. They found thismethod effective for detecting valve failure and that it proved a promising approachto detect other engine faults. Bogus and Merkisz (2005) performed non-linear signalanalysis using ´ the deterministic chaos theory on the vibration signal of a locomotivediesel engine. Based on the analysis, they showed that it is possible to detect misfire ina diesel engine. Carlucci et al. (2006a) evaluated the effect of pilot injection, injectionpressure and timing on the vibration of an engine block using time-frequency andFourier analysis. They observed and detected a change in pilot injection timing andduration at the highest injection pressure through vibration signals. They suggestedthat vibration signals could be used to find a reliable relation with the engine parame-ters and to build cheap sensors as the engine performance indicator.

Li et al. (2010) took advantage of the empirical mode decomposition using thecrank angle domain to detect clearance-related faults in a diesel engine-generator.Their method showed capability to extract the impacts produced by vibrations and tospecify the occurrence in time. Delvecchio et al. (2010) focused on the vibration-basedfault diagnostic of a diesel engine in a cold start test cycle. They analysed the signalsacquired by an accelerometer attached to an engine block using first- and second-ordercyclostationarity based on the Antoni et al. (2002a) study for the purpose of qualitycontrol. They reported this method as “a powerful tool in vibration based diagnostic”.Chiavola et al. (2012) performed an experiment on a two-cylinder CI engine with acommon rail injection system. They found the start of combustion using an accelerom-eter along with a pressure transducer. Moosavian et al. (2016) investigated the impactof piston scuffing defects on engine vibration signals. Using the continuous wavelettransform, they discovered that piston scuffing excited a certain frequency band. Liet al. (2016) utilised the chaotic fractal method to propose a diagnostic method basedon vibration signal. Their results indicated that the maximum Lyapunov exponentis a reliable approach to identify the fuel system fault in the CI engine. Zhao et al.(2017) proposed an algorithm based on empirical mode decomposition to extract thestart of combustion, location of peak pressure and maximum pressure rise rate. They


2.3. Engine structure-borne vibration and acoustic emission 15

found the location of the aforementioned parameter with 0.6°, 0.3° and 0.5° of crankangle, respectively. Although these researchers had varying degrees of success, all ofthem stated that the vibration signal can be used as a reliable indicator of the engineperformance monitoring and fault diagnosis. Most of them showed that analysingthe vibration signal in the crank angle domain is more convenient compared to timedomain due to the crank shaft speed fluctuation. Also, the vibration signal alone(typically in frequency domain) can be used to monitor engine performance.

2.3.2 Structure-borne acoustic emission

According to the British Standards Institution, acoustic emission is defined as “tran-sient elastic waves generated by the release of energy within a material or by a process”(ISO, 2014). The structural acoustic emission method has been generally used forhealth monitoring of engine pumps and bearings; however, this field is relatively newfor the condition monitoring of IC engines.

In 1999, Fog et al. (1999) presented a successful approach based on AE for thedetection of cylinder misfire and exhaust valve leaking in a two-stroke four-cylinderCI marine engine. They analysed signals using principal components analysis andclassified faults with neural network analysis. As a result, they reported the acquiredAE signals are more desirable than other sensors such as vibration, temperature andpressure for condition monitoring of reciprocating machinery. Sharkey et al. (2000)employed in-cylinder pressure, engine block vibration and acoustic emission signalsto train an artificial neural network for classifying faults in a four-stroke two-cylinderdiesel engine. Their results showed that AE signals revealed the best result for combus-tion related faults compared with vibration and pressure signals. In a similar approach,AE signal energy clearly changed between normal condition and faulty exhaust man-ifold in a high speed direct injection diesel engine (Frances et al., 2004). El-Ghamryet al. (2003) analysed the AE signal in the time domain and indicated that differentmechanical events can be identified in parallel. In another study, El-Ghamry et al.(2005a) employed cepstral analysis to indirectly determine the in-cylinder pressuretrace of a small four-stroke and a large two-stroke from the acquired AE signals. Theyfound this analysis works well when the energy level of the AE signal is low.

Pontoppidan et al. (2005) used independent components analysis to identify faultsusing AE signals. Their study was more focused on comparing different methodsrather than definite illustration of the condition monitoring. Nivesrangsan et al. (2007a,2005a) investigated the acoustic emission mapping of two CI engines from wave prop-agation of the engine block. They used a nine-AE-sensor array around the engine block



to localise the source of AE, and to measure the attenuation factor. Moreover, theydeveloped a velocity- and energy-based approach to find the AE signal source location.These approaches were useful to identify single source signals and multiple sourcesignals, respectively. Elamin et al. (2010) found the AE signal with time domain andfrequency domain analysis as an impressive fault monitoring tool in order to observefaulty exhaust valves.

Lin and Tan (2011a) studied the AE signals of a 4-cylinder Perkins CI engineand compared it to in-cylinder pressure and vibration signals. They employed thesynchronous averaging method in the angle domain to decrease noise in the signals.They reported that the AE signal is more satisfactory in comparison with vibrationsignals due to high signal-to-noise ratio. Furthermore, Lin et al. (2011) made a faultyinjector by grinding off the injector pintle head in cylinder one. They observed thatthe AE sensors produced the desired result for detecting the simulated faulty injectorcompared to the pressure signal. They concluded that the fault may have insignificantimpact on the combustion process and the in-cylinder pressure is not a good faultindicator in this case. Lowe et al. (2011) explored the effects of knock level in a 6-cylinder Cummins CI engine using neat diesel and 30% and 50% fumigated ethanolblended with diesel. Their results indicated that AE measured signals on the engine todetect knocking events, while on the other hand, the sensor on the engine head had apoor result. Also, Lowe et al. (2015) reported that valve lash due to excessive clearanceis detectable in the measured AE signal from the engine block, and the impact of pistonslap is marginally observable at the intake and exhaust stroke.

Wu et al. (2015a) presented a technique based on blind source separation and pencillead break to separate and normalise the measured AE signal from a 4-cylinder dieselengine in order to overcome the non-linearity of signals. They showed that eachcylinder can separately be monitored using this technique. In a recent study, Dykasand Harris (2017a) utilised the synchronous average of the RMS AE signals for enginecondition monitoring purposes. They placed four AE sensors on different parts of asingle cylinder diesel engine, and found that AE signals are highly variable in engineoperation parameters such as speed and load. As discussed in this section, there area reasonable amount of studies on the fault diagnosis and condition monitoring of theIC engines. However, there are a few works that investigate the effect of biodiesel onthe AE signal. Hence, this subject requires more consideration, and may increase ourknowledge of the performance of engines using alternative fuels.


2.4. Signal processing 17

2.4 Signal processing

There has been a growing interest in the development of the non-intrusive techniqueto monitor the engine (Mahdisoozani et al., 2019). Non-intrusive techniques can bedivided into two groups. The first group uses the engine block vibration and crankshaft fluctuation to monitor engine performance (Delvecchio et al., 2018). The secondgroup utilises the engine exhaust emission parameters, such as NOx sensors or ion-current sensors, to evaluate the performance of the engine (Gao et al., 2013). Each ofthese techniques can be used as an indication of the combustion process quality, andfurther, to control and optimise engine performance. As mentioned in Section 2.2, in-cylinder pressure is one of the major engine parameters that indicates the combustionquality, although the pressure transducer is intrusive and expensive. Using a cheapnon-intrusive method to measure the pressure is an efficient way of monitoring andcontrolling the combustion. A strong and meaningful relation between the engineblock vibration and in-cylinder pressure was reported in previous studies (Antoni et al.,2002b, Delvecchio et al., 2018). Hence, it is sensible to correlate and reconstructthe pressure with engine block vibration using an accelerometer or AE sensors. Thepossibility of this issue is discussed in the following paragraphs.

One of the earliest studies was conducted by Azzoni (1997) using a linear corre-lation between the signal of a single accelerometer and an in-cylinder pressure sensor.His methodology was based on the Fourier series expansion of two signals. For afour-cylinder SI engine, he showed that a small number of tests at various speeds canbe adequate to estimate the transfer function. Gao and Randall (1999) proposed atechnique called Time Domain Smoothing. This technique first finds the frequencyresponse function of the pressure to the vibration, takes the Laplace transform of it,measures the pressure signal by vibration signal and transfer function in s-plane, andthen uses the inverse Fourier transform to recover the source spectrum. This methodgives a smoother signal compared with Azzoni’s method. Du et al. (2001) reproducedthe pressure signal using the power spectrum of the vibration signals and established anon-parametric model with in-cylinder pressure through a radial basis function (RBF)network. They concluded that the accuracy of this technique is low because of the non-linearity and low signal-to-noise ratio of the vibration signal. To overcome this issue,they suggested applying more training data and the use of a proper filter to extract thecharacteristics from the vibration signals.

Antoni et al. (2002a,b) did a comprehensive study on the vibration signal of theCI engine and introduced the cyclostationary process to analyse the signals. They em-ployed a cyclostationary process with a deconvolution problem to reconstruct the pres-sure signals using the engine block vibration. Their method was successful, especially



through the use of an inverse filter, where they addressed two major problems that makepressure measurement though vibration difficult. Firstly, most of the pressure traceenergy is in the low frequencies (less than 500 Hz) which are only weakly conveyedthrough the rigid block of the engine. Secondly, the acquired vibration signals hadadditive significant noise that were produced by inertial forces, valves opening andclosing, piston slap, etc.

Johnsson (2006) established a non-linear model of the in-cylinder pressure throughboth engine vibration and speed fluctuation using a complex valued RBF network.The input of this model is the discrete Fourier transforms of the speed and vibrationsignals that make the RBF task simple. Although the study showed a promising result,Johnsson proposed that a larger RBF network will result in a more accurate result.Other studies that used RBF neural networks are Bizon et al. (2011) and Jia et al.(2014). Trimby et al. (2017) developed an approach based on a time-delay feedfor-ward neural network to reconstruct pressure using a shaft encoder and knock sensor(block vibration). They applied this approach on a three-cylinder direct-injection sparkignition engine, and compared the result of crank fluctuations and block vibrations.They found that the pressure estimation using the crank kinematics is more accuratethan the measured vibration. Overall, all these methods are acceptable and feasible toreconstruct the pressure by carefully choosing the range of the vibration frequency andplace of the accelerometer. However, the main issue with these methods is that theyare only applicable for the tested engine and cannot be generalised to other engines. Inother words, all these methods are on an ad hoc basis that, optimistically, they can beapplied to engines that are identical to the tested case.

2.5 Knock

Knock is a high pitch metallic sound that is perceived when spontaneous combustionoccurs in the end-gas region of a spark ignition engine (Heywood, 1988). Knock canlead to potential damage to engine components and reduce engine life (Zhen et al.,2012). Manufacturers limit the compression ratio and volumetric efficiency to avoidknocking, which decreases the engine’s thermal efficiency (Wang et al., 2017). Differ-ent sensors have been used to detect and characterise knocking events: accelerometers, piezoelectric transducers, ion current sensor and fibre-optical sensor (Zhen et al.,2012). Accelerometer (vibration) sensors are the most commonly used sensors in sparkignition cars to detect knock (Cavina et al., 2017). They can determine the indirecteffects of spontaneous ignition such as an intense block vibration (Carlucci et al.,2006a). Ion current and fibre optical sensors can demonstrate the location and time


2.6. Misfire 19

of the auto-ignition with a good accuracy when they are fine-tuned (Zhen et al., 2012).However, they cannot provide enough information to characterise the knocking event.In-cylinder pressure evaluation has been found to be the best technique to characteriseknock. The severe pressure wave caused by auto-ignition can be acquired throughthe pressure transducer with high accuracy (Wang et al., 2017). The severity of theknocking event is typically assumed to have a direct correlation with the intensity ofthe resultant effect of the auto-ignition. Even though this assumption seems reasonable,it cannot be proved as there is no available method to directly evaluate the severity ofthe end-gas hotspot. Thus, the validation can only be carried out based on methodsmentioned above (Shahlari, 2016).

Knock onset (KO) and knock intensity (KI) are two properties derived from in-cylinder pressure transducers to address knock (Shahlari and Ghandhi, 2012, Shu et al.,2013). These measurements indirectly indicate the auto-ignition timing and severity,respectively. The amplitude and energy of the signal oscillation can approximate theseverity of spontaneous ignition (Brunt et al., 1998, Worret et al., 2002). The knockidentification has mostly been limited to a threshold definition based on one knockmetric (Wang et al., 2017). The knocking event can be identified when that thresholdis passed. This technique of knock detection has fundamentally poor quality to detectsince it is sensitive to the cycle variation and the sensor noise (Shahlari and Ghandhi,2012). The knocking misdetection or false detection can lead to severe damage orthe loss of efficient power, respectively (Wu, 2007). Assigning the threshold too lowcan also decrease thermal efficiency and increase fuel consumption. Thus, decreasingthe likelihood of false detection of knocking is important to protect the engine fromdamage while improving the engine performance (Angeby et al., 2018). Furthermore,the classification of knock can help to show the severity of the knocking event. It canmake it possible to run the engine with slight or moderate knocking for a short period,which can improve the engine’s overall efficiency.

2.6 Misfire

On-board diagnostics II legislations require OEM to detect continuous misfire (Togaiet al., 2007). Engine misfire can increase exhaust emission and damage different en-gine components specifically catalytic converters. To detect misfire, many techniqueshave been presented in the literature based in engine shaft angular torque, speed andacceleration measurement, engine block vibration measurement, oxygen sensor andexhaust pressure (Mohammadpour et al., 2012).

Engine exhaust pressure method achieve reasonable results; however, this method



is not capable of localising the misfire (Jiang et al., 2008, Willimowski and Isermann,2000). The instantaneous crankshaft speed-based methods have gained more attentionin the literature and industry, since crankshaft encoders have a low cost and high dura-bility (Liu et al., 2013, Williams, 1996). To identify misfiring events in a multi-cylinderengine, an index based on CA angular velocity or acceleration have been calculated andoptimised for each cylinder based on cylinders firing order information (Assaf et al.,2011). These methods adjust the index using real-time and post processing approachesfor each engine operation. On the other hand, normalising the misfire detection indicesis a tedious task for all the engine operation zones and increases the cost of calibration(Guo, 2017). A few studies utilised accelerometers and showed successful results. Acombination of machine learning (ML) methods and accelerometers are used to detectmisfire (Kawamura et al., 2004, Sharma et al., 2014). Although these methods weresuccessful, ML methods are required to be tuned for all the operation events, as well,for the real-time use.

The misfire detection performance is affected by engine load and speed, cycle-to-cycle variation, and driveline oscillations (Wang and Chu, 2005). A method that candetect misfire independent of these variables can effectively increase the performanceof misfire detection. Methods based on the angular measurement of the crank shaftare more sensitive to engine operating conditions. Accelerometer or acoustic emissionsensors can have better performance. However, an analysis technique is required toincrease the accuracy of misfire detection for real-time use.

2.7 Research gap and conclusion

Internal combustion engines have been extensively used in ships, on roads, and instationary power generation due to their excellent durability, high energy density, highefficiency and torque characteristics. Despite of all these advantages, the IC engine isa source of pollution, which can affect human health and the environment, especiallyin urban areas. These pollutants include carbon dioxide, oxides of nitrogen, carbonmonoxide, hydrocarbons and particulate matter. Legislators have been trying to directengine manufacturing industries to produce more efficient and cleaner products. Thus,engine design becomes more complex, and many sensors are added to new enginesfor monitoring and controlling purposes. Using low cost techniques and less sensorswith an acceptable performance can benefit both the engine industries and consumersby allowing real-time monitoring of the engine. Moreover, it can reduce the overallprice of such products. Some of new regulations, such as On-board diagnostics IIlegislation, require OEMs to continuously monitor the engine performance and isolate


2.7. Research gap and conclusion 21

faults to improve overall efficiency and decrease exhaust emission. Hence, there is aneed for sensors that can diagnosis engine faults in parallel.

While there are many studies on engines using biofuel, there is no studies thathave been done to show a comprehensive relationship between a wide range of engineparameters. This can provide a thorough understanding of exhaust emission formationand combustion. A comprehensive study is required to show the relationship betweenengine performance parameters, exhaust emissions and practical sensors such as in-cylinder pressure transducers and acoustic emission sensors. The study can show theusefulness of these sensors for engine health monitoring and diagnostics purposes.

In recent years a number of researchers have employed various methods to moni-tor engine performance, such as cylinder pressure-based methods, crank angle-basedmethods, exhaust ion-current-based methods and vibration-based methods. Each ofthese methods has advantages and disadvantages. A small number of these methodsare used in engines currently in the market. Among them, pressure transducers andaccelerometers are the more popular. However, pressure transducers are used in limitedapplications and the accelerometers are only used as knock detectors in some sparkignition engines. Therefore, a more comprehensive study is required to improve theefficacy of these sensors. The use of acoustic emissions is an emerging field whichshows promise to be a low cost sensor that can be just as useful - or even moreuseful - than the accelerometer. Acoustic emission sensors have been mainly used todetect mechanical faults. However, the AE sensor is also capable of being used as analternative to in-cylinder pressure transducers. Hence, a fundamental study is requiredto look at the relationship between these two sensors and to assess the reconstructingof in-cylinder pressure using AE emission.

Among engine combustion faults, knocking and misfire have a more dominanteffect on engine performance and exhaust emission. Finding these faults is difficultin current engines due to the engine’s complexity. Knocking was typically measuredby one indicator and detected from a predefined threshold. This means that the engineloses some of its power. Therefore, to improve knock detection, a more efficient andreliable method is required by considering more knock indicators. Also, classificationof knock can help to provide more information useful in increasing engine perfor-mance. Misfire causes engine performance to degrade and increases engine pollutants,especially hydrocarbons and particulate matter. A low cost sensor that can detectmisfire will benefit the engine industry to improve engine efficiency.



23

Chapter 3

Effect of bio-fuels on engine performance andemissions

Multivariate analysis of performance and emission parameters in a diesel engine usingbiodiesel and oxygenated additive

Published in:

Energy, Conversion and Management

Authors and affiliations:

Mohammad Jafari1,2, Puneet Verma1,2, Timothy A. Bodisco3, Ali Zare3, Nicholas C.Surawski4, Pietro Borghesani5,Svetlana Stevanovic3, Yi Guo2, Joel Alroe2, ChiemeriwoOsuagwu2, Andelija Milic2, Branka Miljevic2, Zoran D. Ristovski1,2, Richard J. Brown1

1Biofuel Engine Research Facility (BERF) and 2International Laboratory of Air Qual-ity and Health (ILAQH), Queensland University of Technology, Brisbane, Queensland4000, Australia3Flow, Aerosols and Thermal Energy Group (FATE), Deakin University, Geelong,Victoria 3216, Australia4Centre for Green Technology, School of Civil and Environmental Engineering, Uni-versity of Technology Sydney, New South Wales 2007, Australia5School of Mechanical and Manufacturing Engineering, University of New SouthWales, Sydney, New South Wales 2052, Australia


24 Chapter 3. Effect of bio-fuels on engine performance and emissions

Statement of contribution of co-authors for thesis by published paper

The authors listed above have certified that:

1. they meet the criteria for authorship in that they have participated in the concep-tion, execution, or interpretation of (at least) that part of the publication that lieswithin their field of expertise;

2. they take public responsibility for their part of the publication, while the respon-sible author accepts overall responsibility for the publication;

3. there are no other authors of the publication;

4. potential conflicts of interest have been disclosed to (a) granting bodies, (b) theeditor or publisher of journals or other publications, and (c) the head of theresponsible academic unit; and

5. consistent with any limitations set by publisher requirements, they agree to theuse of the publication in the student’s thesis, and its publication on the QUTePrints database.


25

The authors’ specific contributions are detailed below:

Contributor Statement of contributionMohammad Jafari Contributed to the experimental set-up, conducted

experiment, preformed data analysis, and wrote themanuscript

SignaturePuneet Verma Assisted with experiment, collected TEM samples and

preformed image analysisTimothy A. Bodisco Assisted with in-cylinder pressure signal analysis, and

extensively revised the manuscriptAli Zare Assisted with experiment and exhaust emissions data

analysis, and extensively revised the manuscriptNicholas C. Surawski Assisted with principal component analysis, and revised

the manuscriptPietro Borghesani Assisted with acoustic emission signal processing, and

extensively revised the manuscriptSvetlana Stevanovic Leaded and assisted with experimentYi Guo Assisted with experimentJoel Alroe Assisted with experiment and AMS setup, interpreted

the AMS data, and extensively revised the manuscriptChiemeriwo Osuagwu Assisted with experimentAndelija Milic Analysed AMS dataBranka Miljevic Revised the manuscriptZoran D. Ristovski Contributed to the overall study design, supervised

the project, aided with development of the paper andextensively revised the manuscript

Richard J. Brown Contributed to the overall study design, supervised theproject, aided with data analysis and development of thepaper, and extensively revised the manuscript

Principal Supervisor Confirmation

I have sighted emails or other correspondence from all co-authors confirming theircertifying authorship.

Professor Richard Brown 21/09/2020

Name Signature Date





Abstract

Rising concerns over environmental and health issues of internal combustion engines,along with growing energy demands, have motivated investigation into alternative fuelsderived from biomasses, such as biodiesel. Investigating engine and exhaust emissionbehaviour of such alternative fuels is vital in order to assess suitability for furtherutilisation. Since many parameters are relevant, an effective multivariate analysis toolis required to identify the underlying factors that affect the engine performance andexhaust emissions. This study utilises principal component analysis (PCA) to presenta comprehensive correlation of various engine performance and emission parametersin a compression ignition engine using diesel, biodiesel and triacetin. Results showthat structure-borne acoustic emission shows a strong correlation with engine param-eters. Brake specific NOx, primary particle diameter and fringe length increases byincreasing the rate of pressure rise. Longer ignition delay and higher engine speedscan increase the nucleation particle emissions. Higher air-fuel equivalence ratio canincrease the oxidative potential of the soot by increasing fringe distance and tortuosity.The availability of oxygen in the cylinder, from the intake air or fuel, can increasesoot aggregate compactness. Fuel oxygen content reduces particle mass and particlenumber in the accumulation mode; however, they increase the oxygenated organicspecies. PCA results applied in this study show the increase of soot particles reactivityby fuel oxygen content provided by particle chemical and physical characteristics.

3.1 Introduction

The complex nature of compression ignition (CI) engine emissions has been a concernfor decades owing to adverse health effects on humans and consequences to the envi-ronment (Kittelson, 1998, Ristovski et al., 2012, Vaughan et al., 2018). Combustionin a compression-ignition (CI) engine produces particles, gaseous compounds, andvapour phase compounds that may adsorb and/or condense onto particle surfaces uponcooling in the exhaust system and during dilution (Frank et al., 2013, Sun et al.,2010). Governments strictly regulate these emissions with increasingly stringent rules,representing a real challenge to automotive original equipment manufacturers (OEMs)(Johnson and Joshi, 2018, Rashedul et al., 2014).

In recent years, biodiesel has received significant attention as an alternative fueldue to the growing energy demand and decreasing fossil fuel energy resources andrising concerns over environmental and health issues (Hosseinzadeh-Bandbafha et al.,2018, Manaf et al., 2019). Some countries passed the legislation to use a blend of


3.1. Introduction 27

biofuels and fossil fuel in near future (Carneiro et al., 2017). For example, in transportsector, the legislators in Europe require the use 10% of biofuel blends by 2020 andUnited States require the utilisation of 25% of biofuel by 2022 (Carneiro et al., 2017).These fuels have the potential to be utilised directly in internal combustion engines orblended with fossil fuels (Giakoumis et al., 2012, Zare et al., 2017a). Biodiesel andbiodiesel blends lead to a fuel product with different physical and chemical propertiescompared with that of conventional diesel (Knothe and Razon, 2017). Biodiesel hasoxygen group in its chemical bonds that has been shown to be a dominant factor toreduce the exhaust emission (Zare et al., 2016). The oxygen content of biodiesel leadto the reduction of particulate mass (PM); however, it raises nitrogen oxides emissions[15]. Also, biodiesel has smaller greenhouse gas emission footprint since it is derivedfrom renewable resources (Ribeiro et al., 2007). Hence, investigating the engine andexhaust emission behaviour of such biofuels is vital to assess their suitability for furtherutilisation.

Engine performance, fuel parameters, exhaust emissions and their characteristicsneed to be simultaneously considered to obtain a comprehensive insight into pollutionformation in the engine (Johnson and Joshi, 2018). This can be achieved by utilisingan effective multivariate analysis to identify the underlying factors that affect the en-gine performance and exhaust emission (Lim et al., 2007). In the area of CI engineresearch, a range of multivariate techniques have been used for different purposes,for example principal component analysis (PCA) (Clairotte et al., 2012), preferenceranking organization method for enrichment evaluations and geometrical analysis forinteractive aid (PROMETHEE-GAIA) (Surawski et al., 2013), analysis of variance(ANOVA) (Pinzi et al., 2013), and multiple regressions analysis (Pey et al., 2009).Among these methods, PCA is more favourable as it simplifies a complex set of data byreducing the dimensions of the original data into a smaller sets of dimensions (principalcomponents) containing the most information of the original data set (Jackson, 2005).

Principal component analysis (PCA) is a widespread multivariate analysis that havebeen used in many fields (Meglen, 1992). PCA is a dimension reduction method whichrepresents the interrelated data based on the largest variations or principal components(Jolliffe, 2011). It was mainly used in chemometric studies to reveal the correlation,anti-correlation of parameters or complete lack of correlation (Bro and Smilde, 2003).This method primarily keeps most of the original information while removing noisefrom data. If the first few principal components contain a high level of variance fromthe original data set, they can be used to find the correlations between parameters (Gilet al., 2019, Jackson, 2005). Parameters that have similar behaviour will be grouped to-gether. A few studies have used PCA for internal combustion engine research (Clairotteet al., 2012, McDonald et al., 2004, Popovicheva et al., 2017, Rocha and Correa, 2018);



however, to the knowledge of authors, there is no paper in the literature investigatedthe correlation of an extended engine exhaust emissions and performance parameters.

McDonald et al. (2004) conducted an interesting study using PCA and PLS toshow the correlation between particles, semi-volatile organics species and their toxicitydetermined by rat lung tissue damage and inflammation, and mutagenicity in bacteria.Exhaust emission were sampled from both diesel and gasoline vehicles. Their analysisshowed that the chemical species are strongly varying with toxicity. Also, it hasability to produce models to predict the samples relative toxicity with an acceptableaccuracy. Overall, they found the PCA analysis indicates ability to extract the responseassociated with different composition, even if the exposure nature is complex.

Clairotte et al. (2012) performed experiments on two-stroke mopeds due to theirhealth concern and pollutants emission. They used different emission analyser suchas NOx analyser, ionization mass spectrometer, aerosol mass spectrometer and multi-angle absorption photometer. The exhaust particles are physically characterized bycondensation particle counter and fast mobility particle sizer. They used PCA tofind the correlation between different exhaust emissions. The PCA analysis revealedthe strong influence of the exhaust temperature on polycyclic aromatic hydrocarbons(PAH) and particles emission.

Popovicheva et al. (2017) studied a heavy-duty CI engine operated in steady-stateand transient conditions while it was fuelled by diesel, 30% biodiesel blended withdiesel and neat biodiesel. PCA was successfully applied to discriminate the engineoperating conditions, with a higher variance given by the fuel, therefore allowingto better evaluate the environment. Their findings indicated the potential impact ofbiodiesel blending on the chemical characteristics of CI engine exhaust emissions.The importance of health-related properties assigned to functionalised structure andmolecular species of engine-produced particles indicates that their composition maybe significantly changed upon transition from diesel to alternative fuels.

Rocha and Correa (2018) studied the sources of metallic elements in diesel par-ticulate matter emissions by trucks and buses fuelled by biodiesel blends from 5% to20%. They used PCA to group the metal compounds based on engine speed and fueltype. They found a strong effect of engine speed on parameters. Also, they showed areduction in the exhaust emission of metallic elements except lead by blending dieselwith biodiesel.

This study focuses on using correlation analysis, PCA and hierarchical clustering.Diesel and biodiesel with triacetin as a highly oxygenated additive were utilised andarranged in order of fuel oxygen content. A low volume of triacetin was added to


3.2. Materials and methodology 29

biodiesel to study the effect of oxygen in CI engine (Odibi et al., 2019, Zare et al.,2016). While most of studies were focused just on a few engine performance or emis-sion parameters, this study considers a wide range of parameters from six main groups:engine performance, in-cylinder derived data, structure-borne acoustic emission (AE),fuel properties, exhaust emission, and particle morphology and nanostructure. AE wasused to investigate the feasibility of such sensors to monitor the engine performanceand emissions. The engine emission was focused on NOx and particulate matter. Anoted advantage of this investigation is to study engine particle emissions in detailconsidering the chemical composition and physical properties of particles. This pro-vides useful information on oxidation reactivity of particles using oxygenated fuel,which is important in improving the efficiency of diesel after-treatment systems (Guoet al., 2019). This study presents a comprehensive understanding of the relationshipsbetween forty engine parameters which could benefit automobile industries for adapt-ing oxygenated fuel to CI engines.

3.2 Materials and methodology

3.2.1 Observations and parameters

Six different fuels were chosen for this study arranged by oxygen content as shownin Table 3.1. Diesel (D) and coconut biodiesel (B) were used as base fuels and tri-acetin (T) was utilised as the oxygenated additive to biodiesel. Fuels were neat diesel,D80%-B20% (B20), D50%-B50% (B50), neat biodiesel, B96%-T4% (B96T4) andB90%-T10% (B90T10), by volume. For each fuel, the combination of six loads andspeeds were tested to illustrate different engine operating conditions. Thus, thirty-sixcombinations of fuel and engine condition were investigated. All the fuel blends weremade from the same batch. The engine inlet temperature and the temperature of thetest cell remained constant during all the experiments. Before each experiment, theengine was fully warmed up until the engine oil and water temperature reached 90°Celsius. Then, the experiments started in the following order: 75% load at 1500 rpm,50% load at 1500 rpm, 25% load at 1500 rpm, 100% load at 1500 rpm, 100% load at1800 rpm and 100% load at 2000 rpm.

The parameters under investigation are shown in Table 3.2. The parameters arecategorised into six groups: engine performance, in-cylinder derived data, structural-borne acoustic emission, fuel properties, exhaust emissions, and physical properties ofparticle emission. In-cylinder pressure, along with crank angle and injector data, wasused to determine different combustion parameters such as ignition delay and rate of



Table 3.1: Fuel propertiesDiesel1 B20 B50 Biodiesel2 B96T4 B90T10 Triacetin3

Density [kg/m3] 837.90 842.32 848.95 860.00 872.00 890.00 1160.00Kinematic viscosity [cSt] 2.64 3.08 3.73 4.82 4.94 5.12 7.83HHV [MJ/kg] 44.79 43.81 42.35 39.90 39.03 37.72 18.08LHV [MJ/kg] 41.77 40.86 39.49 37.20 36.38 35.16 16.78Cetane number 53.30 54.36 55.95 58.60 56.86 54.24 15.00Stoichiometric Air to Fuel ratio 14.66 14.22 13.56 12.49 12.15 11.66 6.04C [wt. %] 86.35 84.30 81.26 76.31 75.89 73.82 49.54H [wt. %] 13.65 13.35 12.89 12.15 11.85 11.41 6.47O [wt. %] 0.00 2.36 5.84 11.54 12.26 14.77 43.99Providers: 1Caltex® Australia, 2Suncoast Renewables, 3Redox Pty Ltd.

pressure rise (Bodisco et al., 2013, 2012). For this study, 3000 cycles were acquired ateach experimental condition and their average value were considered in the analysis.Structural-borne acoustic emission was acquired using an acoustic emission sensor.It can acquire high frequency pressure waves emanating from the engine block. Inthis study, two characteristics of the signal were selected: the root mean square andmaximum envelope of the signal. The former represents the power of the signal andthe latter is an indication of the maximum amplitude of the signal. The envelope AEsignal is determined by Hilbert transform of the signal (Borghesani et al., 2013, Jafariet al., 2018).

For the exhaust emissions, two important CI engine emissions are investigatedhere: NOx and particles. The chemical composition and physical properties of particlesare investigated in detail. The chemical composition is determined by an AMS (He-dayat et al., 2016). The AMS is most sensitive to aerosol with diameters above 100nmand therefore best represents the composition of accumulation mode aerosol (Liu et al.,2007). The physical properties of particles are found from TEM images post processedby an in-house image processing technique (Verma et al., 2019c). These data are takenfrom the published study by Verma et al. (2019b). Where necessary, the emission datais reported in the standard unit of grams per kilowatt hour. In total, forty parametersare considered in this investigation, as shown in Table 3.2.



Table 3.2: Parameters used in multivariate analysisType Parameter Abbreviation Unit

Engineperformance

Speed Spd revolution per minute (rpm)Brake power PwrB kilowatt (kW)Torque Trq Newton-metre (Nm)Fuel flow rate qF litre per minute (l/min)Injection pressure InjP Mega-Pascal (MPa)Exhaust temperature TExh degree Celsius (°C)Boost pressure Bst kilo-Pascal (kPa)Brake thermal efficiency ηB %Air–fuel equivalence ratio λ –

In-cylinderderived data

Indicated work IW kilo-Joule (kJ)Indicated mean effective pressure IMEP kPaPeak pressure PP kPaStart of injection SOI degree of crank angle (°ca)Start of combustion SOC °caIgnition delay IgnD °caMaximum rate of pressure rise RPRmax kPa/ °ca

Acousticemission

Signal maximum envelope Envmax Volt (V)Signal root mean square RMS V

Fuelproperties

Brake specific fuel consumption BSFC gram per kilowatt hour(g/kWh)

Density ρF kilogram per litre (kg/l)Oxygen content OxyR %Kinematic viscosity νF centistokes (cSt)

Exhaustemissions

Brake specific CO2 CO2 g/kWhBrake specific NOx NOx g/kWhParticulate matter (1 micrometres or less in diameter) PM g/kWhParticle number concentration PN number per kWh (#/kWh)Accumulation mode particle number concentration PNA #/kWhAccumulation mode count median diameter CMDA nanometre (nm)Nucleation mode particle number concentration PNN #/kWhNucleation mode count median diameter CMDN nmTotal organics Org g/kWhNitrates NO3 g/kWhf44–Ratio of oxygenated organic marker to total organics f44 –f57–Ratio of hydro-carbon organic marker to totalorganics f57 –

Particlemorphologyandnanostructure

Primary particle diameter Dp nmFractal dimension FD –Radius of gyration RGy nmFringe length fL nmFringe tortuosity fTr –Fringe distance fDis nm



3.2.2 Experimental setup

This investigation was carried out in the Biofuel Engine Research Facility (BERF)at the Queensland University of Technology (QUT) employing a common-rail, tur-bocharged, after-cooled, six-cylinder diesel engine (Cummins ISBe220-31) (Bodiscoand Brown, 2013, Zare et al., 2017b). The engine brake power is absorbed by an elec-tronically controlled water-brake hydraulic dynamometer. The experimental schematicdiagram is shown in Figure 3.1 and an overview of the facility follows.

DEKATIi

Dyno

Inje

cto

r Se

nso

rP

ress

ure

Tra

nsd

uce

r

Cra

nk

An

gle

Enco

der

PC

DAQ

AE

Sen

sor

Dilution Tunnel

CO2

CA

I ND

IR

NOx

CA

I CLD

CO2

SAB

LE

TEM Samples N

AS

CO2

SAB

LEOrg NO3

f44 f57AM

S

PM PN PNA PNN

CMDA CMDN

DM

S50

0

Air supply

HEP

A

Air supply

HEP

A

MicroscopyImage

Analysis

Dp FD Rgy

fL fTr fDis

1 2 3 4 5 6

Figure 3.1: Experimental setup

An in-cylinder pressure transducer installed in the first cylinder of this researchengine (Kistler piezoelectric transducer type-6053CC60). The crank angle was mea-sured by a rotary encoder (Kistler type-2614) with a resolution of 0.5 degrees (Bodiscoand Brown, 2013). Structural-borne acoustic emission was acquired using a generalpurpose acoustic emission (AE) piezoelectric sensor (Physical Acoustics R15α). TheAE sensor was mounted on the engine block close to the first cylinder where thepressure transducer is installed. These data were acquired synchronously using twoNational Instrument data acquisition boards (NI-9223 synchronised by a NI-9250)utilising LabVIEW® software. The acquisition data rate in this study was one millionsamples per second to satisfy the Nyquist sampling theorem for the full frequencyband of the AE sensor (Shahriar et al., 2017). Other sensors were also installed onthe engine to record exhaust temperature, charged air flow, fuel consumption, boostpressure, common-rail pressure, engine speed and torque.

Two California Analytical Instruments (CAI) series 600 – non-dispersive infrared(NDIR) and chemiluminescence detector (CLD) – were used to measure the CO2 and



NOx in the exhaust, respectively. The NDIR was directly connected to the exhaust.A dilution tunnel was used to dilute the engine exhaust with filtered zero air. Inorder to obtain the dilution ratio, a SABLE (CA-10) was utilised to measure CO2

after the dilution tunnel. The CLD and nanometre aerosol sampler (NAS–TSI 3089)were used after the dilution tunnel. The NAS captured and collected particles on holeycarbon grids for morphological and nano-structural analysis by transmission electronmicroscopy (TEM) (Verma et al., 2019a). A fast particulate analyser, DMS500, wasused to measure the particle number size distribution of the exhaust from which the sizedistribution can be integrated to obtain particle number concentration. An Aerodyneaerosol mass spectrometer (AMS–Aerodyne Research Incorporation) was utilised toanalyse the chemical composition of non-refractory exhaust particles (Stevanovic et al.,2013). Since the AMS is mainly designed for sampling at the atmospheric concentra-tion level, a two stage dilution –consisting of a dilution tunnel and a DEKATI diluter(D, mI-1000)– was utilised. The DMS500 took samples at the same point as the AMSto have comparable measurements from these two instruments.

3.2.3 Data analysis techniques

A major aim of this study is to find the relationship between parameters shown inTable 3.2. Pearson’s correlation of these parameters, principle component analysis andhierarchical clustering are used to explore these relations. All the analysis in this studywas performed in MATLAB®. The data used in this study can be found in AppendixA.

The correlation between parameters are determined by Pearson’s correlation coeffi-cient (Chapra, 2012), Equation 3.1. It indicates the non-dimensional linear dependencyof two parameters with the same number of observations. In other words, the correla-tion coefficient explores the variation of two parameters by scaling the covariance overthe standard deviation (Olofsson, 2012). Thus, this can be a straightforward and usefulmethod in engine research where parameters of various units exist.

corr(Vn, Vm) =cov(Vn, Vm)

σVnσVm(3.1)

where Vs are the parameters, m and n are the number of parameters and cov

is the covariance. When a large number of parameters are available, the correla-tion coefficient produces a large matrix of relationships between each two parameterscombination. Using a dimensionality reduction method, such as PCA, can make theinterpretation of data more effective.



A range of mathematical methods are available to find the principal componentsof a set of observations (Jackson, 2005). One of the basic and well–known methodsis singular value decomposition (SVD). SVD is a class of Eigen value problems inmathematics and is computationally efficient. This method is applicable when thereare no missing values in the data matrix. As shown in Equation 3.2, it decomposesmatrix X into two sets of orthogonal matrices –Y and U– and one diagonal matrix, Σ

(Abdi and Williams, 2010). SVD can be represented as:

X = YΣU′ (3.2)

where Y is left singular vectors matrix, U is right singular vectors matrix and isknown as a loading matrix. Σ is a diagonal matrix and contains the eigenvalues–or explained– of XXT. The major advantage of SVD is that it can decompose Xin one operation without the need to determine a covariance matrix (Jackson, 2005).However, if the parameters have different units, they need to be scaled to a standardizedto unit variance.

One of the common methods of scaling the data is the standardised z-score. Asshown in 3.3, this method transforms x with the mean µ and the standard deviationσ to z with the mean equal to zero and the standard deviation equal to one (Larsenand Marx, 2005). z-score keeps the data distribution unchanged. It is an acceptablepre-processing method of PCA and suggested by Bro and Smilde (2003).

zi,j =xi,j − µj

σj(3.3)

where z is the transformed parameter corresponding to x. i indicates the number ofobservation and j shows the number of parameter.

While PCA can cluster parameters that correlate with each other, one may find ithard to show the PCA result in three dimensions. Thus, an unsupervised clusteringmethod is used in this study to show the clusters. Hierarchical clustering identifiesgroups of parameters which have minimum distance from each other. Initially, itconsiders each parameter as one cluster and, then, merges the clusters together withthe minimum distance from each other. It continuous to link the clusters togetheruntil one cluster remains (Vesanto and Alhoniemi, 2000). This method is used as acomplement to PCA in order to demonstrate the result more clearly. 3.2 illustrates theanalysis diagram of the current study.

Most of experiments are generally subjected to errors and uncertainties (How et al.,2018). In the engine research, the uncertainties of experimental result are mainly due



Figure 3.2: Analysis diagram of this study

to experiment procedures, measurement equipment calibration and condition, and fuelselection. To minimize the uncertainties by experiment procedure, the experimentalprocedure remained the same for all tested fuel. A day before testing a fuel, the enginefuel line was cleaned and the new fuel is pumped to the line. Then, the engine was runfor one hour to make sure that the previous fuel was completely replaced by the newfuel in the engine. On the experiment day, the engine was started and warmed up forhalf an hour or until the engine oil and water temperature reaches 90°C, and, then eachtest was performed. The measurement equipment were serviced and calibrated beforethe experimental campaign. Since there is some variation between each batch of fuel,the same batch of each fuel was considered during the experimental campaign.

Uncertainty arises in PCA when the considered number of PCs do not explain alarge variance of whole data set or the number of observation is small compared withthe number of parameters (Jackson, 1993). Different methods have been proposed inthe literature to select the number of meaningful principal components such as “screetest” or “proportion of variance accounted for” (O’Rourke et al., 2005). In the screetest, the eigenvalues associated with each PC are plotted against PCs and the first PCscan be chosen that have relative larger eigenvalues compared with remaining PCs (Zhuand Ghodsi, 2006). With other methods, PCs, which account for a minimum specifiedpercentage –e.g. 5% or 10%– of the total variance in the data, can be retained, whilethey represent at least 70% in total. In this study, the first three principal componentswere chosen that explain 79.2% of the total variance of the dataset that satisfy the bothcriteria above as shown in Figure 3.3. Furthermore, using these PCs, the data canbe interpreted in a meaningful way (Worthington and Whittaker, 2006). While moreparameters than samples are considered here, PCA is still valid and can be employedas the total variance of first three PCs are large enough compared to the others (Jungand Marron, 2009). Thus, they can be used to explore the relationship between the



parameters–using loadings plot.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Principal component

0

5

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40

Eig

enva

lue

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10

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70

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Var

ianc

e ex

plai

ned

%

Figure 3.3: Eigenvalues associated with principal components on left and cumulativevariance explained by PCs on right.

3.3 Results and discussion

The correlation matrix of all parameters is shown in Figure 3.4. This graph reveals thecorrelation of each pair of parameters. The blue colour indicates correlation of twoparameters, while the red colour shows anti-correlation (inverse correlation) and greyindicates no correlation. The size of the circles depicts the magnitude of the pairwisecorrelation coefficient. The diagonal element is simply one, since it expresses thecorrelation of the parameter with itself.

The correlation matrix is informative in the sense that it shows the correlation ofeach pair of parameters. For example, some engine performance parameters suchas brake power, torque, injection pressure, and exhaust temperature have positivecorrelation together as shown in the top left of Figure 3.4, while they are anti-correlatedwith air–fuel equivalence ratio (λ). On a closer inspection, brake specific CO2 is anti-correlated with the former engine performance parameter and is correlated with λ

Therefore, CO2 and λ can form a group which is anti–correlated with brake powerand torque. It can be concluded at higher load, the availability of air decreases whichcan result in a reduction of brake specific CO2 and possibly an increase of other


3.3. Results and discussion 37

Engine

performance

In-cylinder

derived data

Acoustic

emission

Fuel

properties

Exhaust

emissions

Particle

morphology

and

nanostructure

Figure 3.4: Correlation matrix illustration

emissions such as hydrocarbon and/or carbon monoxide. While the cluster of engineperformance parameters is clear, it may be difficult to identify and classify othervariables. For example, fractal dimension (FD), which quantitatively characterise theshape of aggregated particles, is anti–correlated with engine performance parameters,similar to CO2 and λ. However, FD shows correlation with fuel parameters such asoxygen ratio while CO2 and λ do not show significant correlation with them. In thiscase, FD can be affected by different parameters. PCA can facilitate the interpretationof the result by decreasing the dimensions of the data and emphasising the largestvariation as shown by loadings plot in Figure 3.5.

Figure 3.5 defines the linear relationship between the parameters presented by eachparticular principal component. The parameters that lie near each other vary together.The parameters located opposite each other are anti–correlated. The parameters thatare neither correlated nor anti-correlated do not depend on each other. In this case,they are located in the range 45 to 135 degrees of each other in a plane of two principalcomponents.



As discussed in the methodology, the clusters are determined by hierarchical clus-tering using the loading value of the first three principal components. The dendogramof the latter analysis is presented in Figure 3.6. In this figure, the horizontal axisshows the parameters and cluster while the vertical axis represents the distance –in thiscase Euclidean distance– based on the first three PCs (Murtagh and Legendre, 2014).The longer the vertical line, the more dissimilar the two parameters or clusters are.Although this technique clusters the parameters, it cannot set the number of clusters.The number of clusters is chosen in order to have minimum significant clusters whichindicate more similarity –i.e. a shorter vertical axis.

PM

CMDN

CMDA

PNA

OrgT

HPC

3 -

15.

14%

NO3

RGy

PN

D

DP

f57

fL

F

SOISOC

CO2

RMS

B

GRPRmax

fTr

PC 2 - 25.17%

TExh

C

NOX

IMEPIWTrq

Envmax

PNN

A

PPBst

PwrB

IgnD

SpdB

FD

fDis

qF

InjP

f44

E

BSFC

PC 1 - 38.89%

F

FOxyR

Figure 3.5: Loadings Plot of the first three principal components

The dark red cluster (A) on the right side of Figure 3.5 presents most of the engineperformance data together with AE indicators and some of the in-cylinder parameterssuch as peak pressure and indicated mean effective pressure. This cluster is anti-correlated with the orange cluster (C). Cluster C includes CO2, λ, fTr and fDis. Cluster Hprimarily represents the parameters correlated with PM, such as Org, CMDA, CMDN



IW IME

PT

rq

TE

xh

Pw

rB

Env

max

Bst

PP

qF

InjP

B

RM

SS

pd

IgnD

PN

N

CO

2

fTr

fDis

PN

f57

PN

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NO

X

f44

FD

BS

FC

F

Oxy

R

F

RP

Rm

ax

DP

fL S

OI

SO

CP

M

CM

DA

Org

T

CM

DN

NO

3

RG

y

AB C D

EF

GH

Figure 3.6: Dendrogram of hierarchical clustering based on the first three principalcomponents

and RGy. The latter cluster inversely varies with the green cluster (E). This clustercontains fuel properties as well as NOx, FD and f44. The purple cluster (B) in thelower centre includes Speed, PNN and IgnD. This cluster is anti-correlated with twoclusters shown in light blue (F) and dark blue (G). Group G is formed by just twoparameters, SOI and SOC. Cluster F includes RPRmax, Dp and fL. This cluster alsoshows anti-correlation with cluster E especially in the PC1–PC2 plane. Cluster Dcontains PN, PNA and f57 and is anti-correlated with E and B. An overall interpretationof Figure 3.5 is given in Table 3.3 and Table 3.4. Table 3.3 shows the inter-correlationof parameters in each cluster. Table 3.4 shows the correlation and anti–correlation ofpairs of clusters and describes the main interpretation. A detailed explanation of theimportant parameters is presented as follows.

All engine performance variables are grouped together except speed and air–fuelequivalence ratio (λ) in Figure 3.5. While the engine load is increasing, it uses morefuel and supplies higher power and torque. The brake thermal efficiency (ηB) ofthe tested engine increases with engine load (Zare et al., 2018). Relationship be-tween engine performance parameters are consistent with generally accepted dieselengine performance parameters (Agarwal, 2007, Ashraful et al., 2014, Graboski andMcCormick, 1998, Xue et al., 2011). Generally,λ has an optimal value between itsmaximum and minimum for the best engine performance. However, in this study,the variation of λ was affected by oxygenated fuels which is the reason λ is anti-correlated with engine variables such as torque and fuel injection pressure. Structural-borne acoustic emission (AE) indicators, nitrate oxides (NOx), fractal dimension (FD),fringe length (fL), brake specific fuel consumption (BSFC) and indicated parametersare other parameters that are correlated or anti-correlated with engine performance.These parameters will be discussed in more details in below.

The fuel properties parameters considered in this study are fuel density (ρF ), oxy-gen weight ratio (OxyR), kinematic viscosity (νF ). These four variables make a group



Table 3.3: General interpretation of parameters in each clusterCluster Parameters Interpretation Significance

A

PwrB, Trq, qF,InjP, TExh, Bst,ηB , IW, IMEP,PP, Envmax,RMS

More fuel and supplies higher powerand torque. IW, IMEP, and PP individ-ually represent the engine performance.The injection of more fuel in to thecylinder results in higher PP and astronger acoustic signal propagates inthe engine structure.

The engine performance parameters areconsistent with literature. An elaboratesignal processing tool can be employedto reconstruct in-cylinder pressure usingacoustic emission sensor.

B Spd, IgnD, PNNAt higher speed, IgnD is longer due toadvanced SOI.

Longer ignition delay and higher enginespeeds can increase the nucleationparticle emissions.

C λ, CO2, fTr, fDistHigher λ increases particles fringedistance and tortuosity.

Higher λ can increase the oxidationpotential of the soot by increasing fDisand fTr

D PN, PNA, f57The variation in PN is mostly derivedby PNA. f57 contributes to the largerparticles.

Correlation of f57 with PNA shows thatthe hydrocarbons are condensed moreon larger particles that brings them intothe measurement range of the AMS.

ENOX, f44, FD,BSFC, ρF ,OxyR, νF

Oxygenated fuel can increase NOx,f44 and FD. The positive correlationof BSFC variation with other fuelparameters show that by increasingfuel oxygen content, fuel consumptionincreases.

NOx increasing trend is consistent withliterature. Oxygenated fuel particleemission are more reactive. Oxygenatedfuel produces more compact particles.

F RPRmax, DP, fL

Higher RPRmax causes a rapid in-crease in temperature during combus-tion, which increases DP and fL .

Initial stage of combustion affects Dpand fL. Note that this observation hasbeen made in diffusion flame studies andhas been shown here for CI engine.

G SOI, SOC Start-of-combustion varies with start-of-injection.

SOI influence on SOC agrees withexpectation.

HPM, CMDA,CMDN, Org,NO3, RGy

Increase in CMDs indicate larger par-ticles and contributes to larger PM andRGy. When PM is larger, there are moreOrg and NO3 available.

PM –measured by DMS500- and RGy –measured by TEM- are proportional toAMS mass basis measurement – organicand nitrate.

with NOx, FD and f44 in Figure 3.5. Biodiesel and triacetin have higher ρF , νF andOxyR, and, hence, these values are grouped together (Demirbas, 2009, Knothe, 2005).The positive correlation of BSFC variation with other fuel parameters show that byincreasing fuel oxygen content, fuel consumption increases (Lapuerta et al., 2008, Zareet al., 2016). Also, the lower heating value (LHV) of the biodiesel and triacetin areless than diesel that lead to the increase of BSFC and the reduction of engine power(Canakci, 2007, Shahabuddin et al., 2013).

The in-cylinder parameters are determined using cylinder pressure trace and crankangle (Bodisco et al., 2013, 2012). In Figure 3.5, indicated work (IW), indicated meaneffective pressure (IMEP) and peak pressure (PP) are in a group with the engine per-formance parameters as they individually represent the engine performance (Heywood,1988). Start-of-Injection (SOI) and start-of-combustion (SOC) make a separate clusterwhich is anti–correlated with speed and ignition delay (IgnD). At higher speed, theengine management system advances SOI, as a result SOC. This can lead to stretchIgnD as well (Malbec et al., 2016).

Maximum rate of pressure rise (RPRmax) is positively proportional to SOI and SOC,



Table 3.4: The relationship between pairs of clustersPair Clusters Correlation PCs Comment

I A, C Anti-correlated 1, 3λ is anti-correlated with engine parameters, which means that theavailability of air decreases at high engine loads. This results inmore brake specific CO2.

II A, H Uncorrelated 1,2 The reduction in particle mass (H) is dominated by oxygen contentof fuel rather than engine performance parameters (A).

III B, D Correlated 1,3 At higher speed and longer ignition delay, the engine producesmore PN in total.

IV B, H Anti-correlated 2,3 This engine produces less PM at higher speed and longer IgnD.

V B, G Anti- correlated 1,2,3At higher speed, the engine control unit advances SOI, as a resultSOC. This causes to stretch IgnD. Advanced SOI results in lessPNN.

VI C, E Correlated 1,2Air-fuel equivalence ratio is higher for oxygenated fuel, due tooxygen content. Brake specific fuel consumption is proportionalto CO2.

VII C, F Anti-correlated 1,3 Smaller λ can result in bigger Dp. By FL, fTr and fDis increases,which make the particle more disordered.

VIII D, H Correlated 1,2 PNA has major contribution to PM.

IX D, E Anti-correlated 1,2,3

Oxygenated fuel produce less particle number in the accumulationmode. Hydro-carbon organics decrease with increase in the oxygencontent of fuel. The increase in PNA combined with a smaller FDgives a larger surface area for the condensation of higher volatilityhydrocarbons (f57).

X E, H Anti-Correlated 1,2Oxygenated fuel produces less PM and, hence, less Org andNO3. CMDs of accumulation and nucleation modes decreases byincrease of fuel oxygen content.

XI E, G Uncorrelated 1,2,3 Start of injection (G) is controlled by engine control unit, and thefuel type does not have an influence.

and anti-correlated to IgnD and speed. Figure 3.4 shows not only engine load but alsoengine speed can have an effect on RPRmax. At Lower engine speed, more time isavailable for fuel to combust that results in the higher RPRmax. Longer residence timeand higher RPRmax cause higher in-cylinder temperature that increases the formation ofNOx (Aithal, 2010, Horibe and Ishiyama, 2009). On the other hand, shorter residencetime and lower RPRmax can lead to less complete combustion (Rabl et al., 2015).Also, there is less time for agglomeration of soot, and, hence, the particle numbermay increase.

Root mean square (RMS) and maximum envelope (Envmax) of AE signal are used inthis study to investigate the relationship between these two parameters and the engineperformance data, especially in-cylinder peak pressure (PP). As shown in Figure 3.5,RMS and Envmax remain in the same cluster as PP. RMS, Envmax and PP have a largecontribution to build PC1 along with other engine performance parameters. Based onthis and Figure 3.4, it can be concluded that the variation of the AE signal is highlycorrelated with the variation of peak pressure. When PP is higher, a stronger acousticsignal propagates in the engine structure.

Here it is shown that the AE sensor is capable of acquiring a signal that can beutilised to monitor the engine performance and, possibly, combustion. The informationprovided by PCA can be used to construct an empirical model using some statistical



modelling tools such as principal component regression (PCR) or partial least squareregression (PLS) (Frank and Friedman, 1993, Wehrens and Mevik, 2007). The modelcan be used to estimate the engine performance and pollution parameters correlated oranti-correlated with RMS or Envmax such as the engine power or NOx. This model canbe physically justified –yet empirically fitted–, since the in-cylinder parameters affectNOx emission and AE strongly correlates with PP, IW and IMEP. AE indicators arecapable of reconstructing the in-cylinder pressure signal (El-Ghamry et al., 2005b).The number of principal components is important to make a reliable model. Moreelaborate signal processing tools such as autoregressive moving average (ARMA)or neural network (NN) can be employed to reconstruct in-cylinder pressure usingacoustic emission sensors (Zhang, 2003).

Based on the first three principal components, brake specific nitrogen oxides (NOx)is grouped with fuel parameters, fractal dimension and f44. NOx shows an increasingtrend in the presence of fuel oxygen content, which is consistent with the literature(Giakoumis et al., 2013, Sun et al., 2010, Varatharajan and Cheralathan, 2012). BasedFigure 3.4, NOx is anti-correlated with the engine performance parameters such asbrake power and torque and correlated with λ. Also, Figure 3.4 reveals the dependencyof NOx to the maximum rate of pressure rise (RPRmax), ignition delay (IgnD) and speed.At lower speed, NOx increases, as it is time available for NOx formation (Palash et al.,2013). RPRmax is proportional to the instantaneous temperature and, as a consequence,to NOx (Higgins et al., 2000, Hoekman and Robbins, 2012). Furthermore, NOx showscorrelation with FD and f44 in Figure 3.4, since all these parameters are correlated withthe fuel oxygen content.

Brake specific particle mass (PM) is located in cluster H, where it has a significantcontribution to PC2 in Figure 3.5. PM –measured by DMS500– and RGy –measuredby TEM– are proportional to AMS mass basis measurement–Org and nitrate NO3

(Canagaratna et al., 2007). Increase in CMDs indicate larger particles which contributeto larger PM and RGy (Park et al., 2012). All these parameters are anti-correlatedwith fuel properties parameters as shown in Figure 3.4 and Figure 3.5. It has beenshown in the literature that the oxygenated fuel can decrease PM (Lapuerta et al., 2008,Saxena et al., 2017, Verma et al., 2019d, Zare et al., 2017c). PM mainly varies with thefuel parameters while it shows insignificant correlation with the engine performanceparameters. PM is correlated with particle number in accumulation mode in Figure3.4. This indicates that particles in accumulation mode are the dominant contributor tothe particulate mass.

Total particle number (PN) is grouped with PNA and f57 in Figure 3.5. PN isdetermined by accumulation particle number (PNA) and nucleation particle number



(PNN). Therefore, PN can be expected to be located between PNA and PNN. Thevariation PN is mostly derived by PNA since they are grouped together (Tan et al.,2014). Figure 3.4 reveals that the PN increases at higher speed, where there is shortercombustion duration (Wang et al., 2006). PNN is located in group B with speed andIgnD. Longer IgnD delays combustion and leads to high gas temperature. Highertemperature can increase the soot oxidation process and result in the PNN growth. InFigure 3.4, PNN has a correlation with fuel properties such as fuel oxygen content. Itindicates that biodiesel can generally increase ultrafine particle emissions (Chien et al.,2009).

The relationship between particle chemical composition and fuel properties showsthat OxyR, ρF , and νF affect the chemical composition of compounds condensed onparticles (Hedayat et al., 2016). In Figure 3.5, Org, NO3 and f57 are anti-correlatedwith fuel parameters, and f44 is grouped with fuel parameters. Oxygenated fuel leadsto formation more oxygenated species on particles (Stevanovic et al., 2013). Thisis shown by the increase of f44 which represents the oxygenated organic aerosol(Hedayat et al., 2016). In Figure 3.4, f44 is anti-correlated with speed which mayindicate that by decreasing the combustion duration the oxygenated organic aerosoldecreases. f57 is clustered with PN and PNA which indicates that the ratio of hydro-carbons in exhaust is increasing when PN increases. Correlation of f57 with PNA

shows that the hydrocarbons are condensed more on larger particles, bringing theminto the measurement range of the AMS (Liu et al., 2007).

Primary particle diameter (Dp) is grouped with RPRmax and fringe length (fL) inFigure 3.5. It illustrates that the initial stage of combustion affects Dp and fL. Ac-cording to Figure 3.4, the variation of Dp is proportional to RPRmax. Higher RPRmax

lead to rapid rate of fuel burning (Jaaskelainen and Khair, 2017). Thus, more fuel isprobably trapped in the middle of the diffusion flame, where the temperature is highand there is not enough oxygen (Botero et al., 2019). This can boost the pyrolysis andnuclei process as a precursor to create larger Dp (Tree and Svensson, 2007). Dp is anti-correlated with OxyR which indicates that oxygenated fuel can provide more oxygenin the diffusion flame and may reduce the pyrolysis of the fuel. While the latter istrue, the formation of the Dp is not only dependent on the available oxygen as it is alsorelated to the initial stage of the combustion (Hirner et al., 2019).

Figure 3.5 shows that fractal dimension (FD) generally increases with the increaseof fuel oxygen content or, in other words, fuel oxygen content can produce morecompact particles (Tzamkiozis et al., 2011, Verma et al., 2019b). FD is correlatedwith λ in Figure 3.4. This can be due to more availability of charged air in the cylinderthat results in the more compact soot aggregates (Hura and Glassman, 1989). Also,



FD is anti-correlated with engine parameters, mainly PP and the exhaust temperature(TExh). At higher pressure and temperature, coagulation and agglomeration of particleclusters increase that result in chain-like structure of particles and smaller FD (Tree andSvensson, 2007, Verma et al., 2019b). No correlation is found with speed and initialcombustion parameters since soot aggregates form in the second stage of combustionin the chamber and, later, in the exhaust (Tree and Svensson, 2007).

Radius of gyration (RGy) is in the same group with PM in Figure 3.5. This showsthat the larger the particles, the larger the radius of gyration. Also, RGy inverselyvaries with fuel properties. Fringe length (fL) is grouped with RPRmax and Dp. Byan increment of RPRmax, fringe length can increase while it is anti-correlated withspeed and ignition delay. fL is also increasing with the pressure which agrees with(Li et al., 2011) study. On the other hand, it is anti-correlated with fringe tortuosity(fTr) and fringe distance (fDis). Hirner et al. (2019), Jiang et al. (2019) observed thesame relation between fL, fTr and FD. These relationships can show that by increasingboth pressure and temperature in the premixed combustion phase, fL can increase and,hence, results in oxidative resistance of exhaust nanoparticle (Li et al., 2011).

Fringe distance (fDis) and tortuosity (fTr) are grouped with equivalence ratio andCO2, and is anti-correlated with engine parameters in Figure 3.5. When the equiva-lence ratio is higher and more air is available, the particles have higher fringe distanceand tortuosity that result in higher oxidative potential of particles (Jaramillo et al.,2015). Figure 3.4 shows the dependency of fDis on the fuel properties and increaseswith fuel oxygen content that shows higher oxidative potential of particles producedby oxygenated fuel. In Figure 3.4, both fDis and fTr are anti-correlated with RPRmax andcorrelated with speed. These indicate that the structural distorted in particles decreasesat lower speed and higher RPRmax, hence higher temperature (Savic et al., 2016).

This study shows that the fuel oxygen content increases the reactivity of sootparticles provided by particle chemical and physical characteristics f44, fL, fDis, and fTr

(Stevanovic et al., 2013, Verma et al., 2019b). Also, oxygenated fuels produce morecompact and smaller particles evident by FD, CMDs, and Dp. These characteristics ofoxygenated fuels particle emissions can potentially increase the health risk, althoughthey reduce PM (Stevanovic et al., 2013, Vaughan et al., 2019). Proper after-treatmentdevices, consist of diesel oxidation catalyst (DOC) and diesel particulate filter (DPF),can be employed to remove oxygenated fuels particle emissions provided that theefficacy of DPF can be improved by modifying the permeability and porosity of filterchannels (Bensaid et al., 2009, Guo et al., 2019).


3.4. Conclusion 45

3.4 Conclusion

Principal component analysis has been used to identify the relationship between regu-lated and unregulated exhaust emission, engine operating conditions and fuel charac-teristics of a CI engine fuelled by diesel and biodiesel with oxygenated additive. Thesefindings provide more insight into pollution formation in the engine. Results showed:

• Maximum rate of pressure rise is correlated with brake specific NOx and anti-correlated with total particle number regardless of the fuel type.

• Both fDis and fTr increased with shorter combustion duration.

• FDis showed dependency on the fuel properties and increased with oxygen con-tent.

• fL can increase by RPRmax and maximum in-cylinder pressure while it was anti-correlated with combustion duration.

• Formation of the Dp was dependent on the fuel oxygen content and the initialstage of the combustion.

• Soot aggregate compactness increased by the availability of oxygen in the cylin-der, from the intake air or fuel. Also, soot aggregate compactness decreased aspeak pressure and exhaust temperature increased.

• Oxygenated fuel produced smaller particles as evident by count median diame-ter.

• Fuel oxygen content increases the reactivity of soot particles as evident by f44,fL, fDis, and fTr.

• Structure-borne acoustic emission correlated with in-cylinder peak pressure andengine parameters, while it was anti-correlated with brake specific NOx.

This study used only coconut oil biodiesel; however, engine performance and exhaustemissions using biodiesel from different feedstocks can be studied by using the sameanalysis technique as has been presented here. This can show the difference in per-formance of different type of biodiesels. Depending on the chosen parameters, thevariance, that each PCs explains, will be different. More PCs can be consideredas long as it satisfies a criteria for selecting PCs such as the scree test. This studyalso showed that AE sensors can be employed as a diagnostic tool to monitor engineperformance and, hence, emissions. Future work could include an empirical or semi-empirical model of the system based on the statistical and physical relation between



AE indicators, in-cylinder parameters and emission such as NOx. The dimensionallyreduced data set generated by PCA can be utilised to build a model using statisticalmodelling tools, such as principal component regression. Furthermore, other fuel typesand engine operating modes should be considered to develop a more comprehensivemodel.


47

Chapter 4

Pressure reconstruction using acousticemission

In-cylinder pressure reconstruction by engine acoustic emission

Submitted to:

Mechanical Systems and Signal Processing


Mohammad Jafari1,2, Puneet Verma1,2, Ali Zare3, Pietro Borghesani4, Timothy A.Bodisco3, Zoran D. Ristovski1,2, Richard J. Brown1

1Biofuel Engine Research Facility (BERF) and 2International Laboratory of Air Qual-ity and Health (ILAQH), Queensland University of Technology, Brisbane, Queensland4000, Australia3Flow, Aerosols and Thermal Energy Group (FATE), Deakin University, Geelong,Victoria 3216, Australia4School of Mechanical and Manufacturing Engineering, University of New SouthWales, Sydney, New South Wales 2052, Australia


48 Chapter 4. Pressure reconstruction using acoustic emission









49



experiments, preformed data analysis, and wrote themanuscript

SignaturePuneet Verma Assisted with experiment, and revised the manuscriptAli Zare Extensively revised the manuscript and aided with

development of experimentPietro Borghesani Aided with development of paper and data analysisTimothy A. Bodisco Extensively revised the manuscriptZoran D. Ristovski Revised the manuscript and aided with development of

the paperRichard J. Brown Contributed to the overall study design, supervised the

project, aided with data analysis and development of thepaper, and extensively revised the manuscript




Name Signature Date





Abstract

The importance of the in-cylinder pressure transducer has been proven in revealingthe information about combustion and exhaust pollution formation, as well as for itscapability to classify knock. Due to their high price, they are not used commerciallyfor engine health monitoring, which is of significant importance. Hence, this study willinvestigate the reconstruction of the in-cylinder pressure trace using a structure-borneacoustic emission (AE) sensor, which are relatively low cost sensors. As shown inthe literature, AE indicators ,such as the signal root-mean-square, show a strong cor-relation with in-cylinder pressure parameters, such as peak pressure, in both time andcrank angle domain. However, a complete reconstruction of pressure trace can help tocontinuously monitor the engine and derive many other cylinder pressure parameters.In this study, to avoid the effect of engine speed fluctuations, the reconstruction is donein the crank angle domain by means of the Hilbert transform of AE. Complex cepstrumsignal processing analysis with a feed-forward neural network is used to generate areconstruction regime. Furthermore, the reconstructed signals are used to determinesome of the important in-cylinder parameters such as peak pressure (PP), peak pressuretiming (PPT), indicated mean effective pressure (IMEP) and pressure rise rate. Resultsshowed that the combination of cepstrum analysis with neural network is capable ofreconstructing pressure using AE, regardless of engine load, speed and fuel type. Thereconstructed pressure can be used to reliably determine PP and PPT. IMEP can beestimated as well in a reasonable range.

4.1 Introduction

There has been a growing interest in the development of an alternative technique toindirectly monitor engine performance. These techniques can be divided into twogroups. The first group uses the engine block vibration and crank shaft fluctuationto monitor engine performance (Delvecchio et al., 2018). The second group utilisesengine exhaust emissions, such as NOx sensors or ion-current sensors, to evaluate theperformance of the engine (Gao et al., 2013). Each of these techniques can be used asan indication of the combustion process quality and, further, to control and optimiseengine performance. Pressure transducers are widely regarded as the optimal sensorsfor monitoring and fault diagnosis of internal combustion (IC) engines. However, thesetransducers are expensive, which causes them to not be used in commercial vehicles(Mahdisoozani et al., 2019). Using an inexpensive non-intrusive method to measurethe pressure is an efficient way of monitoring and controlling the combustion.



Accelerometer or structure-borne acoustic emission (AE) sensors have been usedto detect in-cylinder related events such as knock and misfire and non-cylinder pressureevents such as vales and injectors vibration response. These sensors can be the alter-native to pressure transducers, as a strong and meaningful relation between the engineblock vibration and in-cylinder pressure was reported in previous studies (Antoni et al.,2002b, Delvecchio et al., 2018, Jafari et al., 2019). To measure the engine combustioneffectively, these sensors need to be optimally placed on the engine structure and theirsignals need to be properly filtered to reduce the effect of non-cylinder pressure events(Vulli et al., 2009). Two non-cylinder pressure events are more significant than theothers, valves closure and fuel injection (Vulli et al., 2009). These two events usuallyhave a higher frequency than combustion and different timing. Therefore, they canbe filtered by a proper low-pass filtering or windowing. This can be done in singlecylinder engines with less complication compared to multi-cylinder engines. Thiscan be addressed in multi-cylinder engines by employing more sensors to isolate eachevents from each cylinders. Overall, it is sensible to reconstruct the cylinder pressurewith engine block vibration or structural pressure wave using an accelerometer orstructure-borne acoustic emission (AE) sensors, respectively.

Azzoni (1997) conducted a study using a linear correlation between the signalof a single accelerometer and an in-cylinder pressure sensor based on Fourier seriesexpansion of two signals. For a four-cylinder SI engine, he showed that a small numberof tests at various speeds can be adequate to estimate a transfer function. Gao andRandall (1999) proposed a technique called time domain smoothing. This techniquefirst finds the frequency response function (FRF) of the pressure to the vibration, takesthe Laplace transform of it, and transfer function in s-plane and, then uses the inverseFourier transform to recover the source spectrum. This method gives a smoother signalcompared with Azzoni’s method. Du et al. (2001) reproduced the pressure signalusing the power spectrum of the vibration signals and established a non-parametricmodel with in-cylinder pressure through a radial basis function (RBF) network. Theyconcluded that the accuracy of this technique is low because of the non-linearity andlow signal-to-noise ratio of the vibration signal. To overcome this issue, they suggestedthe application of more training data and use of an appropriate filter to extract thecharacteristics from the vibration signals.

Antoni et al. (2002a,b) did a comprehensive study on the vibration signal of a CIengine and introduced a cyclostationary process to analyse the signals. They employeda cyclostationary process with a deconvolution problem to reconstruct the pressuresignals using the accelerometer signal. Their method was successful, especially byusing an inverse filter, and they addressed two major problems that make pressuremeasurement though vibration difficult. Firstly, most of the pressure trace energy is



in the low frequencies (less than 500 Hz), weakly conveyed through the rigid blockof the engine. The acquired vibration signals had additive significant noise that isproduced by inertial forces of cylinder valves. El-Ghamry et al. (2005b) reconstructedthe pressure signal during the combustion phase, using an AE sensor and complexcesptrum analysis. They showed that the AE signal has a higher single-to-noise ratiocompared to the accelerometer. The combination of an AE signal and cepstral analysisprovides an acceptable reconstruction; however, this method cannot be generalised fordifferent engine operations.

Johnsson (2006) established a non-linear model of the in-cylinder pressure throughboth the engine vibration and speed fluctuation using a complex valued RBF network.The input of their model is the discrete Fourier transforms of the speed and vibrationsignals that make the RBF task simple. Although his study showed a promisingresult, he proposed that a larger RBF network would result in a more accurate result.Other studies that used RBF neural networks are Bizon et al. (2011) and Jia et al.(2014). They successfully reconstructed pressure; however, their methods were limitedto an specific engine load and speed. Trimby et al. (2017) developed an approachbased on a time-delay feedforward neural network to reconstruct pressure using ashaft encoder and accelerometer. They applied this approach on a three-cylinder direct-injection spark ignition engine, and compared the result of crank fluctuations and blockvibrations. They found that the pressure estimation using the crank kinematics is moreaccurate than the measured vibration. Dunne et al. Dunne and Bennett (2020) proposeda pressure reconstruction method for varying engine speeds and loads based on fournon-linear functions of crank angle, velocity, acceleration and jerk. This methodcould measure the peak pressure and peak pressure timing with 6.5% and 2.7% errorsrespectively.

Most of the studies, which used an accelerometer for pressure reconstruction, re-ported that the accelerometer noise prevents a satisfactory reconstructed signal. On theother hand, AE sensor has a higher signal-to-noise ratio which benefits the pressurereconstruction. The other issue is that these methods reconstructed the pressure in asingle engine operation (one load and speed), and they are not applicable for widerange of engine load and speed. This study uses AE sensor t reconstruct pressure withhigh accuracy and small error. Also, the method proposed method is applicable for awide range of engine operation and different fuel types.

This investigation employs the envelope of an AE signal to reconstruct the pressuretrace of each engine cycle. The complex cepstrum analysis is deployed to find atransfer path based on the deterministic part of AE and pressure signals. The nondeter-ministic part of the signal is highly nonlinear and varies with engine speed, load, and


4.2. Methodology 53

cycle-to-cycle variation. Hence, the transfer path determined by the cepstral analysisis utilised to make an intermediate model of the pressure trace from the AE envelope.Then, these models are used with their corresponding pressure signal to train a feed-forward neural network. This combination of methods can successfully reconstructpressure for a wide range of engine load, speed and fuel type within an acceptableerror range.

4.2 Methodology

4.2.1 Data analysis techniques

The in-cylinder pressure reconstruction is developed based on the engine structure-borne acoustic emission signal in the crank angle domain. While the pressure signalcan be reconstructed from AE in the time or crank angle domain, the reconstructionregime is done in the crank angle domain to remove the effect of engine speed fluctu-ation. Furthermore, the envelope of AE signal (using Hilbert transform) is employedinstead of the raw AE signal. The reason is that the pressure and raw AE signals areuncorrelated in the frequency domain. However, it is shown that the envelope of AEsignal and the pressure signal have similar frequency content which make it applicablefor the pressure reconstruction in this study (El-Ghamry et al., 2005a).

As discussed by Antoni et al. (2002a), a cyclic signal, such as the in-cylinderpressure and acoustic emission of an engine, consists of three parts as show in Equation4.1.

X(θ) = D(θ) +ND(θ) +N(θ) (4.1)

where D is the deterministic part of the signal, ND is the non-deterministic part ofthe signal and N is the noise. The deterministic part is the common part in all the cyclesand can be determined by taking the cyclic average of signals. The non-deterministicpart of the engine signal accounts for the difference between signals due to changes inspeed, load and cycle-to-cycle variation.

A transfer path is developed based on the deterministic part of the in-cylinderpressure and the envelope of AE using a complex cepstrum method. This transferpath can be used to approximate the pressure trace using the envelope of AE emissionas an input. Although this reconstruction can reproduce the general shape of thepressure trace, it cannot provide a reasonable solution. A feedforward neural networkis employed to take into account the non-deterministic part of the pressure trace.

The reconstruction of pressure (X(θ)) from AE signal (Y(θ)) can be considered as a



single-input single-output system with a transfer path of H(θ). Hence, the output signal(AE signal) is the convolution of the transmission path with input signal (pressuretrace) (El-Ghamry et al., 2005a).

Y (θ) = X(θ) ∗H(θ) (4.2)

Equation 4.2 can be taken to the frequency domain using the discrete Fouriertransform (DFT). The frequency domain in here is not temporal and is related to crankangle scale. Since the crank angle signals have an equally-spaced scale, the uniformforward and backward discrete Fourier transform (DFT) algorithm can be used here.DFT converts equally-spaced samples of the pressure and the envelope of AE into anequally-spaced pseudo-frequency domain.

Taking Equation 4.2 to the pseudo-frequency domain, the resultant equation iscalled the frequency response function, which is the common transfer function usedin system identification and modal analysis.

Y (ω) = X(ω).H(ω) (4.3)

A method called complex cepstrum is utilised to solve Equation 4.3.This methodtakes back Equation 4.3 to the crank angle domain while changing the convolution toaddition by taking the logarithm of the signal spectrum.

log {Y (ω)} = log {X(ω)}+ log {H(ω)} (4.4)

To find the transfer path, Equation 4.4 is transformed to crank angle domain by theinverse of DFT.

Yceps(θ) = Xceps(θ) +Hceps(θ) (4.5)

whereYceps(θ) = f−1[log {Y (ω)}]

Xceps(θ) = f−1[log {X(ω)}]

Hceps(θ) = f−1[log {H(ω)}]

Hence the transfer path can be easily determined by subtracting the input fromthe output. This transfer path can reconstruct the in-cylinder pressure correctly whenthe variation between each cycle is small. In other words, this transfer path is useful


4.2. Methodology 55

at its corresponding speed and load and it changes by load, speed and cycle-to-cyclevariation.

The non-deterministic part of both pressure and AE are highly non-linear, whichmakes it difficult to achieve a perfect solution. However, these problems can beaddressed by black-box methods such as neural networks. The neural network (NN)has the capability to be trained over a wide range of data and, then to be used for datareconstruction. A significant issue with the NN method is that it is highly sensitiveto noise (Cochocki and Unbehauen, 1993). This is important here as the AE signal isinherently noisy and it is therefore difficult to construct a pressure trace directly fromthe AE signal.

While the reconstructed pressure by the deterministic transfer path is poor, it isless noisy compared with the original AE signal. Hence, these results can be used totrain a NN using the original pressure trace to have an improved solution that will beapplicable for a wide range of engine speeds, loads and fuel types. Therefore, in thiswork, the combination of complex cepstral analysis and a neural network is used toconstruct the pressure signal from acoustic emission. In this study, a shallow feed-forward neural network is applied to the initial reconstructed pressure from cepstralanalysis. MATLAB® neural network time series toolbox is used to train the data.Levenberg–Marquardt training is employed since this algorithm is relatively faster andmore robust than other training algorithm such as Gauss-Newton (Hafner et al., 2000,Hagan and Menhaj, 1994). Ten hidden layers were trained using in-cylinder pressuresignal of different engine speed, load and fuel type. The number of hidden layers inthis study is obtained using a trial-error method. The smaller number of hidden layersresults in less accurate answer while the larger number of hidden layers increases thecomputational time and cause overfitting of the output results. The schematic of NNused in this study is shown in Figure 4.1.

W

b

Activation Function

W

b

+

Activation Function

Input Input

Hidden layer Output layer

10 layers

+ +

Figure 4.1: Feed-forward neural network applied in this study



4.2.2 Experimental setup

This study was performed in the Biofuel Engine Research Facility (BERF) at Queens-land University of Technology (QUT). A single cylinder diesel engine was chosen forthis research. The specification of engine is indicated in Table 4.1. This single-cylinderengine was uncomplicated in design. It was suitable to be used for a fundamental studydue to it being a basic and reliable platform. This engine was controlled by a 120kW eddy-current dynamometer. It was directly connected to the dynamometer via atorsional coupling. The schematic of the setup is shown in Figure 4.2. To investigatea wide range of engine operation, the following engine speeds, loads and fuel typeis considered: Speeds 1300RPM, 1500RPM, 1700 RPM; Loads idle, 2kW, 4kW and8kW; Fuel type Diesel, Diesel with 20% biodiesel (B20), diesel with 50% biodiesel(B50) and neat biodiesel (B100).

The engine was equipped with a Kistler piezoelectric pressure transducer type-6053CC40 with measuring range 0-250 bar. The crank angle was measured by anoptical crank angle encoder with a resolution of 0.5 degrees. Structural-borne acousticemission was acquired using a general-purpose Physical Acoustics piezoelectric sensorR15α with an operating frequency range of 50-400 kHz. The AE sensor was mountedon the engine cylinder head using a magnetic hold-down. It was connected to a dataacquisition board (DAQ) through a AE2A amplifier system. An analogue nationalinstrument DAQ NI-9223 was used to acquire the pressure and AE signals. Thecrank angle signals were acquired using a digital DAQ NI-9250. These two DAQwere installed on NI cDAQ-9178 USB chassis which was connected to a computer.All the data from those two DAQ were synchronously acquired using a LabVIEW®software. The data were collected at a rate of one giga-sample per seconds to ensurethe acquisition of the full AE sensor frequency band.

Table 4.1: Engine specificationModel Cylinders

(L) Capacity Bore×stroke(mm)

Max.power(kW/rpm)

Max.torque(Nm/rpm)

Compressionratio Aspiration

KubotaRT140

SingleCylinder 0.709 97 ×96 10.3/2400 49.03/1600 18 Naturally

aspirated

4.3 Pressure reconstruction

In this study, the envelope of the acoustic emission signal is used to construct thepressure trace. The envelope of AE is more similar to the pressure trace and less noisycompared with the original AE signal in the crank angle domain. The pressure trace,AE signal and AE envelope are shown in Figure 4.3.


4.3. Pressure reconstruction 57

Aco

ust

ic E

mis

sio

n

Crank Angle Encoder

Signal Conditioner Charge Converter

TDC Signal

CA Signal

AmplifierPre-amplifier

Pre

ssu

re T

ran

sdu

cer

Encoder Box

LabVIEW

KUBOTA

DA

Q

Figure 4.2: Sensors setup on the single cylinder engine

0 100 200 300 400 500 600 700Crank angle [deg]

-6000

-4000

-2000

0

2000

4000

6000

Pre

ssur

e [k

Pa]

-6

-4

-2

0

2

4

6

Vol

ts [V

]

Pressue signalAcoustic emission signalEnvelope of AE signal

Figure 4.3: Pressure and acoustic emission signals

There are 48 combinations (observations) of fuel, load and speeds that are consid-ered in this study. Each observation consists of consecutive cycles selected from oneof the combinations of engine speed, load and fuel type. Half of the observations areconsidered to train the algorithm and the other half are used for the validation. Figure4.4 shows the observations that are used. The circles in the figure show the trainingset, and the crosses indicate the validation set.



Idle 2kW 4kW 8kW Idle 2kW 4kW 8kW Idle 2kW 4kW 8kW

Diesel

B20

B50

B100

Training dataValidation data

1300RPM 1700RPM1500RPM

Figure 4.4: The combination of fuel, load and speed used in this study. Circles areused for training and crosses for validation

The pressure and AE signals are respectively band-pass filtered at 10Hz-25kHz and40kHz-90kHz in the time-domain to minimise the signal noise and reduce the effectsof other non-combustion events. Then, the envelope of AE signals is determined usingHilbert transform. To find the deterministic part of signals, a cyclic average of pressureand AE signal was found using the training data. Then, by using the complex cepstrum,the transfer path is determined using Equation 4.5. This transfer function is utilised toconstruct the intermediate pressure signal by using the AE envelope from each cycle.These intermediate pressure signals are then used with their corresponding originalpressures to train a shallow neural network that consists of ten hidden layers. Theresult for one cycle is shown in Figure 4.5. In the figure, the light blue solid line isthe AE envelope. This data is used to generate the intermediate pressure signal (dottedblue line) using the deterministic transfer path. Then, using the trained NN, the finalreconstructed pressure is generated.



0 100 200 300 400 500 600 700Crank angle [deg]

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Pre

ssur

e [k

Pa]

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Vol

t [V

]

Original pressureReconstructed pressure by transfer pathReconstructed pressure after applying NNAcoustic emission envelope

Figure 4.5: Original and reconstructed pressure


The reconstructed pressures based on the cepstrum NN algorithm show a reasonable -in some cases perfect - result. The reconstruction error (RE), Equation 4.6, is used todetermine the error of the present study using the validation set of data.

RE =1

N

N∑n=1

‖Pn,Org − Pn,Rec‖Pn,Org

(4.6)

where N is the number of samples per one combustion cycle, POrg is the originalpressure and PRec is the reconstructed pressure. The histogram of the errors basedon 2400 engine cycles (validation data) is shown in Figure 4.6. Three reconstructedpressures with the error of 3.31%, 5.65% and 20.41% are compared with their originalones in Figure 4.7. Around 87% of cycles have an error less than 10%. This erroris reasonable considering the pressure reconstruction of different engine operationconditions. Most of the higher errors belong to idle cycles. This can be due to highercyclic variation at idling.



5 10 15 20Relative reconstruction error [%]

0

50

100

150

200

250

Num

ber

of c

ycle

s

Figure 4.6: Error of reconstructed pressures obtained from validation data.

0 100 200 300 400 500 600 700

Crank angle [deg]

0

1000

2000

3000

4000

5000

6000

7000

Pre

ssur

e [k

Pa]

Reconstruction Error 3.31%

OriginalReconstructed

0 100 200 300 400 500 600 700

Crank angle [deg]

0

1000

2000

3000

4000

5000

6000

7000

Pre

ssur

e [k

Pa]



0 100 200 300 400 500 600 700Crank angle [deg]

0

1000

2000

3000

4000

5000

6000

7000

Pre

ssur

e [k

Pa]



Figure 4.7: Reconstructed pressures with errors of 3.31%, 5.65%, and 20.41%.



Four in-cylinder pressure parameters are studied using reconstructed pressure forinvestigating the suitability of this method to measure important in-cylinder pressureparameters. These four parameters are peak pressure (PP), peak pressure timing (PPT),indicated mean effective pressure (IMEP), and maximum pressure rise rate (MPRR).These values are determined from the original and reconstructed pressures for eachcycle and compared by their estimated probability density (kernel density estimate) ofthe original and reconstructed values and the probability of their relative error.

Figure 4.8 shows the estimated probability distribution of PP, PPT, IMEP andMPRR with their errors. The black line is the data derived from the original pressureand the blue line is from the reconstructed pressure. The red line shows the relativeerror calculated from each observation. For PP, while the size distribution has somedifferences, most of the error is below 10%. PPT measured by reconstructed pressureshave small errors, mostly below 1%. However, the error of IMEP is larger comparedto PP and PPT. Although the IMEP error is larger, it is mostly below 50% which canstill be meaningful. On the other hand, the reconstructed pressures were not successfulin measuring the MRPP. The reason may be that the reconstructed pressure has highnoise compared to the original signal.

This study was limited to pressure reconstruction in a single-cylinder diesel en-gine. Diesel engines produce sufficiently strong acoustic responses during combustionthat help to reconstruct the pressure signal (Jafari et al., 2018). On the other hand,combustion induced acoustic signal in spark-ignition engines are weak with highercycle-to-cycle variation (El-Ghamry et al., 2005b). Although the AE signal may notbe practical for the in-cylinder pressure reconstruction of spark-ignition engines, it isan appropriate approach to monitor knock (El-Ghamry et al., 2003). The block vibra-tion of spark-ignition engines have been used to reconstruct the in-cylinder pressure(Trimby et al., 2017). Hence, the cepstrum-neural network algorithm provided in thisstudy can be applied on the block vibration measurements as well.



5500 6000 6500 7000 7500 8000Peak pressure [kPa]

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Pro

babi

lity

dens

ity fu

nctio

n

10-3 From original signalFrom reconstructed signal

-10 -5 0 5 10 15 20PP Relative error [%]

0

1

2

3

4

5

6

7

8

9

10

Pro

babi

lity

dens

ity fu

nctio

n

Error

355 360 365 370 375 380Peak pressure timing [CA]

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Pro

babi

lity

dens

ity fu

nctio

n

-1 -0.5 0 0.5 1 1.5PPT Relative error [%]

0

20

40

60

80

100

120

Pro

babi

lity

dens

ity fu

nctio

n

0 200 400 600 800 1000 1200IMEP [kPa]

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Pro

babi

lity

dens

ity fu

nctio

n

10-3

-100 -50 0 50 100IMEP Relative error [%]

0

0.5

1

1.5

2

2.5

Pro

babi

lity

dens

ity fu

nctio

n

0 500 1000 1500MPRR [kPa/CA]

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

Pro

babi

lity

dens

ity fu

nctio

n

-1000 -800 -600 -400 -200 0 200MPRR Relative error [%]

0

0.1

0.2

0.3

0.4

0.5

0.6

Pro

babi

lity

dens

ity fu

nctio

n

Figure 4.8: Probability distribution function of PP, PPT, IMEP and MPRR with theirerrors


4.5. Conclusion 63

4.5 Conclusion

In this investigation, the feasibility of reconstructing the in-cylinder pressure signalusing structure-borne acoustic emission was assessed. The study produced a methodthat can reconstruct pressure using acoustic emission for a wide range of engine speed,load and fuel while other studies just reconstruct the pressure for a few engine loadand single fuel. This is important since this method can consider a wide variationof in-cylinder pressure variation. Furthermore, reconstructed peak pressure and peakpressure timing had a high accuracy with maximum error of ±10% and ± 1%, re-spectively. Complex cepstrum analysis and neural networks were used together todevelop an algorithm for pressure reconstruction. The complex cepstrum analysishas an advantage that converts the convolution to addition. This can help to easilydetermine the transfer path. Although this method is not applicable over differentloads and speeds, it can be used to determine the deterministic transfer path of the data.This can be used to determine an intermediate model of pressure from AE that can beused in NN. NN can effectively deal with non-linear signals while it is sensitive tonoise. The output of the reconstructed pressure from complex cepstrum analysis withits corresponding original pressure were used to train NN based on 24 observations,each of which consists of 100 cycles. Then, another set of 24 observations were used tovalidate the algorithm. The error analysis of the reconstructed data showed that 87% ofreconstructed pressures have an error less than 10%. Furthermore, peak pressure, peakpressure timing, indicated mean effective pressure and maximum pressure rise ratewere determined from original and reconstructed pressures and compared together. Itis shown that PP and PPT can be measured by reconstructed pressure with a goodaccuracy. While the error of IMEP was larger, the results derived from reconstructedpressure were still in a reasonable range. However, the reconstructed pressure failedto determine MPRR due to noise. Overall, in this study, the in-cylinder pressure wassuccessfully reconstructed using an AE signal for a wide range of engine operatingconditions.



65

Chapter 5

Engine knock detection and classification

Knock detection and classification using k-means clustering and k-nearest neighbours

Submitted to:

Applied Thermal Engineering


Mohammad Jafari1,2, Philipp Weber3, Olaf Toedter3, Thomas Koch3, Richard J. Brown1

1Biofuel Engine Research Facility (BERF) and 2International Laboratory of Air Qual-ity and Health (ILAQH), Queensland University of Technology, Brisbane, Queensland4000, Australia3Institut fur Kolbenmaschinen(IFKM) , Karlsruhe Institute of Technology, Karlsruhe,Baden-Wurttemberg, Germany


66 Chapter 5. Knock detection and classification









67


Contributor Statement of contributionMohammad Jafari Preformed data analysis, and wrote the manuscriptSignaturePhilipp Weber Assisted with experiment and development of the paperOlaf Toedter Supervised the project, and revised the manuscriptThomas Koch Supervised the project, and revised the manuscriptRichard J. Brown Aided with data analysis and development of the paper,

extensively revised the manuscript




Name Signature Date





Abstract

New emission regulations will require car manufacturers to reduce CO2 emissions intheir future production. In petrol engines, the main method for decreasing CO2 emis-sions is to increase thermal efficiency; however, the thermal efficiency of SI enginesis limited due to knock phenomenon. This problem can be addressed by classifyingknocking events and decreasing knock misdetection or false detection. Classifyingknock based on its intensity can allow the engine to run on a certain degree of knockfor a short period of time and increase the overall engine efficiency, while it does notcause damage to the engine. Decreasing misdetection and false detection of knockwill help to reduce potential damage to the engine and increase the engine efficiency,respectively. This study developed a novel method that utilised four different metricsbased on the in-cylinder pressure signal at the same time to increase the accuracy ofknock detection. Also, this study established a knock classification technique basedon k-means clustering and k-nearest neighbour. k-means clustering is applied to thesefour parameters to cluster the combustion events with respect to knocking intensity.The k-means result is a reference pattern for k-nearest neighbour classification to detectand classify each engine cycle. This novel combination of detection and classificationmethods can be used as a monitoring tool to provide feedback to the engine controlunit to increase the engine’s thermal efficiency, while preventing engine damage dueto knock.

5.1 Introduction

Spontaneous combustion in the end-gas region of a spark ignition engine can resultin a high burn rate of the air-fuel mixture. This spontaneous ignition accompaniedby a prompt energy release causes an intense propagation of an associated pressurewave in the combustion chamber. Pressure waves generate high frequency vibration,which is transmitted through the engine head and block, and perceived as a highpitch metallic sound called knock (Heywood, 1988). Knock may lead to potentialdamage to engine components and reduce engine life (Pitz and Westbrook, 1986). Thestrong pressure waves and high temperature from this knock phenomenon can resultin further damage such as the erosion of the cylinder head and piston crown, failure ofthe head gasket and piston ring, and piston melting in severe cases (Heywood, 1988).In addition to the possibility of damage, knocking can lead to an increase in NOx andCO emissions. Manufacturers limit the compression ratio and volumetric efficiency toavoid knocking, which decreases the engine’s thermal efficiency (Wang et al., 2017).Due to recent changes in emission regulations, manufacturers need to increase the



combustion efficiency in order to decrease total production of CO2 from SI engines(European Parliament, 2019).

Over the past decades, different sensors have been used to detect and characteriseknocking events: optical sensors, in-cylinder pressure transducers, and accelerometers(Wang et al., 2017). Optical sensors can demonstrate the location and time of theauto-ignition with a good accuracy. Engine optical techniques enable observationof the auto-ignition in detail (Wang et al., 2015). Optical sensors cannot be usedin commercial engines due to complexity and high price. Accelerometer (vibration)sensors are widely used in spark ignition cars to detect knock (Cavina et al., 2017).They can indirectly determine the side effects of spontaneous ignition such as anintense block vibration (Carlucci et al., 2006b). In-cylinder pressure evaluation hasbeen found to be the best technique to characterise knock, since the severe pressurewave caused by auto-ignition can be acquired through the pressure transducer flush-mounted in the cylinder head (Wang et al., 2017, Zhen et al., 2012). The severity of theknocking event is typically assumed to have a direct correlation with the intensity of theresultant effect of the auto-ignition. Even though this assumption seems reasonable,it cannot be proved as there is no available method to directly evaluate the severity ofthe end-gas hotspot. Thus, the validation can only be carried out based on methodsmentioned above (Shahlari, 2016, Zhen et al., 2012).

Pressure transducers are usually employed to find a wide range of informationabout the engine. Common examples are the standard thermodynamic quantities suchas indicated power, indicated mean effective pressure, heat release rate, indicatedthermal efficiency, peak pressure and pressure rise rate (Heywood, 1988). Piezoelectriccrystal based sensors are the most popular pressure transducers for measuring thecylinder pressure (Jiang et al., 2014). These kinds of sensors generate electrical signalswhen a changing force is imposed on them. The signal is not in absolute terms;however, it has a linear relationship with the force or pressure (Bodisco and Brown,2013).

Using in-cylinder pressure transducers, two properties can generally be identifiedto address knock: knock onset (KO) and knock intensity (KI) (Shahlari and Ghandhi,2012, Shu et al., 2013). These measurements indirectly indicate the auto-ignitiontiming and severity, respectively. The auto-ignition causes a sudden increase in thelocal pressure. It generates a pressure wave that propagates through the chamber withthe speed of sound. Thus, a reasonable estimation of the start of the auto-ignition is tomeasure the time or crank angle when the pressure oscillation starts. The amplitude andenergy of the signal oscillation can approximate the severity of spontaneous ignition(Brunt et al., 1998, Worret et al., 2002).



The knock identification has mostly been limited to a threshold definition basedon one knock metric (Wang et al., 2017). The knocking event can be identified whenthat threshold is passed. This technique of knock detection is fundamentally flawedsince it is sensitive to the cycle variation and the sensor noise (Shahlari and Ghandhi,2012). The knocking misdetection or false detection can lead to severe damage orthe loss of efficient power, respectively (Wu, 2007). Assigning the threshold too lowcan also decrease thermal efficiency and increase fuel consumption. Thus, decreasingthe likelihood of false detection of knocking is important to protect the engine fromdamage while improving the engine performance (Angeby et al., 2018). Furthermore,the classification of knock can help to show the severity of the knocking event. It canmake it possible to run the engine with slight or moderate knocking for a short period,which can improve the engine’s overall efficiency.

The main focus of this study is to investigate a number of available knock metricsand to choose between some of them to be used simultaneously to detect and classifyknocking events. Utilising multiple knock metrics improves the capability of detectingknock and, hence, increases the performance of the engine. Knock classificationprovides more information for a flexible knock control strategy rather than a fixedthreshold method. This can help to optimise desired engine damage prevention whiledecreasing fuel consumption.

In this investigation, three fuel types were used at three engine speeds to studytheir effect on knock metrics. Twelve knock metrics will be discussed in brief. Four ofthe metrics are chosen which can effectively identify knock but which have a smallercorrelation with engine speed and fuel type. It is thus shown that they will have morerobust properties. These metrics are used to construct a reference knock classificationbased on k-means clustering. This reference is used by a k-nearest-neighbours modelto detect and classify each engine cycle, which can be appropriate for a real-time knockdetection application.

5.2 Knock metrics

Generally, knock metrics are calculated in the time/crank angle or the frequency do-mains (Shu et al., 2013). Knock metrics are determined using the pressure trace signal,pressure derivatives, filtered signal or other dependent parameters such as heat releaserate (Shahlari and Ghandhi, 2012). One or multiples of these values can be used asan indication of knock. The benefit of using multiple metrics is to reduce the chanceof knock misdetection or false detection (Angeby et al., 2018, Wang et al., 2017). InTable 5.1, different knock metrics used in the literature are shown with their definition


5.2. Knock metrics 71

and references.

Table 5.1: Knock metrics and their definitionKnock metric Abbreviation Definition References

Peak pressure PP The maximum in-cylinder pressureduring combustion

Burgdorf and Denbratt (1997),Chun and Kim (1994), Milloand Ferraro (1998), Naber et al.(2006)

Maximum pressure riserate MPRR The maximum of the in-cylinder pres-

sure derivative before the peak pressureEng (2002), Hou et al. (2010),Xu et al. (2015)

Maximum of heat releaserate MHRR The maximum amount of heat release

rate during combustion

Ando et al. (2015), Hou et al.(2010), Zavala and Folkerts(2011)

Decay rate of heat releaserate DHRR The maximum absolute of the second

derivative of heat release after MHRR

Ando et al. (2015), Matsuuraet al. (2016), Zavala and Folk-erts (2011)

Maximum amplitude ofpressure oscillation MAPO The maximum absolute of band-pass

filtered in-cylinder pressure traceBrunt et al. (1998), Galloni(2012), Xu et al. (2015)

Integral of the modulus ofpressure oscillation IMPO

The Integration of absolute band-passfiltered in-cylinder pressure trace over acertain crank angle before and after thecombustion event

Brecq et al. (2003), Steurs et al.(2014), Worret et al. (2002)

Dimensionless knock indi-cator DKI The ratio of IMPO and the product of

MAPO and IMPO integration windowBrecq et al. (2003), Zhen et al.(2012)

Root mean square of pres-sure oscillation RMS The root mean square of band-pass

filtered pressure Millo and Ferraro (1998)

Maximum power of firstresonance frequency MPRF

The power of the combustion chamberfirst resonance frequency that can bedetermined using fast Fourier transform

Bares et al. (2018), Shen et al.(2019)

Maximum of pressure os-cillation envelope ME The maximum envelope of band-passed

filtered in-cylinder pressure traceRandall and Antoni (2011),Yan and Gao (2009)

Integration of pressure os-cillation envelope IE

The integration of band-passed filteredin-cylinder pressure envelope over acertain crank angle before and after thecombustion event

Randall and Antoni (2011),Yan and Gao (2009)

Knock onset KO

The start of the knocking event is shownin the crank angle domain and is definedas the crank angle where the band-passfiltered pressure passes a predefinedthreshold

Bradley and Kalghatgi (2009),Burgdorf and Denbratt (1997),Shahlari and Ghandhi (2012),Worret et al. (2002)

Peak pressure (PP) is utilised as a knock metric, especially when the pressuresignal is measured at low resolution (Shahlari, 2016). PP is influenced by operatingcondition and subjected to lower frequency engine events (Burgdorf and Denbratt,1997, Shahlari, 2016). The maximum pressure rise rate (MPRR) per unit of crankangle - the first derivative of pressure signal Equation 5.1 - is also used as a knockindicator (Xu et al., 2015). In the case of spontaneous combustion, the rate of fuelburn increases, resulting in the rapid rise of pressure. The rate of pressure rise is alsoaffected by engine load and speed, hence it needs to be specified separately for eachengine working condition.

MPRR = max{dpdθ} (5.1)

Apparent heat release rate, that is determined using Equation 5.2, is also utilisedto quantify knock since the heat release rate is higher in the spontaneous combustiondue to a higher gas burn rate. The maximum of heat release rate (MHRR) and the



decay rate of heat release rate (HRRD) are two indicators that are derived from HRRfor knock identification (Ando et al., 2015, Zavala and Folkerts, 2011).

dQ

dθ=

γ

γ − 1PdV

dθ+

1

γ − 1VdP

dθ(5.2)

MHRR = max{dQdθ} (5.3)

HRRD = max{|d2Q

dθ2|} (5.4)

Maximum amplitude of pressure oscillation (MAPO) is a straightforward metricthat can be obtained from the amplitude of filtered pressure signal of each cycle (Bruntet al., 1998, Galloni, 2012). In some of the literature, the maximum absolute valueof filtered pressure and in some others the maximum of peak to peak signal is used(Galloni, 2012). This indicator shows the amplitude of pressure wave propagationin the cylinder; however, this amplitude is sensitive to the location of the sensor andauto-ignition region (Bertola et al., 2006).

MAPO = max{|PF |} (5.5)

Another indicator is the energy of the filtered pressure signal, which is the inte-gration over the specific range of crank angle where combustion occurs. Hence, it iscalled integral modulus of pressure oscillation (IMPO) and it is proportional to signalenergy of pressure oscillation (SEPO) (Steurs et al., 2014).

IMPO =

∫ θ+∆θ

θ

|PF |dθ (5.6)

The dimensionless knock indicator (DKI) was proposed by Brecq et al. (2003) asshown in Equation 5.7. It shows the ratio of two surfaces where IMPO is the surfaceunder the filtered pressure signal and MAPO×w is the total surface of computationalwindow (w). When the knock is intensified, MAPO is increasing and the denominatorof Equation 5.7 gets larger. Hence, DKI decreases by increasing the knock intensity.

DKI =IMPO

MAPO × w(5.7)

Frequency analysis of the filtered pressure or heat release rate is also used tocharacterise and detect knocking cycles (Bares et al., 2018, Shen et al., 2019). Theintense pressure wave due to knocking phenomenon excites the resonant frequenciesinside the cylinder which can be obtained using a Fourier transform of the filtered


5.2. Knock metrics 73

pressure signal. The theoretical resonance frequency of the cylinder can be calculatedusing Equation 5.8.

fi,j = cBi,j

πDB

(5.8)

where f is the resonant frequency of the cylinder. i and j indicate the mode ofthe resonance. B, c and DB are the Bessel constant, the speed of the sound and borediameter, respectively. Usually, the first mode of the resonance contains most of thesignal power and, hence, it can be used as an indicator for knocking (Bares et al.,2018). In this study, the maximum power of first resonant frequency (MPRF) - whichis around 7500 Hz for the tested engine - is obtained through discrete Fourier transformand used as one of the knock metrics.

All the metrics in Table 5.1 have been used as a knock indicator in the literatureexcept the maximum of pressure oscillation envelope (ME) and the integration of thepressure oscillation envelope (IE). The signal envelope is widely used in the bearingand pump fault detection and diagnosis (Randall and Antoni, 2011). These two indica-tors are derived through the Hilbert transform (HT) (signal envelope) of the band-passfiltered pressure signal. ME is the maximum value of filtered pressure envelope. Thisindicator is proportional to MAPO as it indicates the amplitude of the signal. Thesetwo methods are less sensitive to noise caused by sensors and acquisition systems. IEis the integration of the signal envelope over the crank angle where the combustionoccurs, 20º CA before top dead centre to 60º CA after top dead centre. This indicatoris proportional to the total power of the signal.

It is possible for knock onset to be shown in the time or crank angle domain;however, crank angle can be a better indicator due to removing the variation of theengine shaft speed. The threshold value exceeded method (TVE) identifies the crankangle of the band-passed filtered pressure where a pre-determined threshold is passed.Hence, this crank angle will be considered as KO. Although TVE is a straightforwardmethod, it may detect KO late by a few crank angles degrees. The reason is that thethreshold is a constant value and should be specified in order to prevent false detection.Another similar method is to consider the crank angle where the signal of a high-pass-filtered heat release rate passes the x-axis, before the threshold is exceeded (Bradleyand Kalghatgi, 2009, Worret et al., 2002).

Other methods make use of the pressure signal first and third derivatives to detectKO. In both methods, a threshold is specified for the tested engine. The point where thederivatives exceed the threshold is considered as KO (Burgdorf and Denbratt, 1997).It should be noted that these methods are sensitive to the sampling rate. Signal energyratio is another method to measure KO, which is the energy of the pressure signal



after knock onset over the energy of the noise (before the knock onset). This methodincludes an altered signal to noise ratio by considering a span of crank angle or timebefore and after the onset. However, whilst it is ineffective for real-time purposes, itcan be a useful and accurate tool for an offline diagnosis (Shahlari and Ghandhi, 2012).

For clarity, the knock indicators discussed in this section are shown in Figure 5.1for no knock, moderate knock and intense knock events. Four graphs are providedfor each event. The first row of graphs shows the pressure trace between 10º beforeTDC to 50º after TDC. Peak pressure (PP) and maximum pressure rise rate (MPRR)show higher values by increasing the intensity of knock. The second row shows theapparent heat release rate (AHRR) with two indicators: the maximum heat releaserate (MHRR) and the decay rate of heat release rate (HRRD). As expected from thepressure trace, MHRR is higher for the intense knock. HRR decays more slowly forthe no-knock event. The next row is the band-pass filtered (BPF) pressure signal –highpass 3 kHz and low pass 20 kHz. The BPF signal is shown in black and the envelopeof the signal is shown in blue. The maximum envelope (ME) shows a big differencebetween knocking and no knocking events. The knock onset is also shown for bothknocking events; however, this value cannot be found for no-knock since the signalnoise and pressure fluctuation from combustion are in a similar range. The last rowshows the spectrogram of BPF pressure. It is obvious that the knocking events havehigher power of the first resonant frequency - around 7.5 kHz compared to no-knock.In the intense knock, the auto-ignition also excites the second mode of the chamberresonance.

5.3 Methodology

5.3.1 Source of data

Experimental data of the Institute of Internal Combustion Engines (IFKM) at the Karl-sruhe Institute of Technology (KIT) served as an input for the analyses in this studyBanzhaf (2016). The experiments were performed on an engine test bench equippedwith a downsized and turbocharged single cylinder direct injection SI engine. Thereby,the effect of different ethanol-containing fuels on knocking was investigated. Pressureindication data was obtained with a resolution of 0.05 °CA, which corresponds toa recording frequency of 420 kHz at the highest considered engine speed of 3500rpm. For various fuels, knocking was provoked at different engine speeds throughincreased boost pressures or advanced ignition timings, amongst others. According toBanzhaf (2016), the operating conditions were adjusted until at least 1% of the cycles


5.3. Methodology 75

(a) No-knock (b) Moderate knock

(c) Intense knock

Figure 5.1: Engine pressure data set used for knock metrics shown at 1500 rpm fromE25. Row 1: In-cylinder pressure. Row 2: Apparent heat release rate. Row 3:Band-pass filtered in-cylinder pressure. Row 4: Spectrogram of band-pass filteredin-cylinder pressure.



exceeded knock intensity threshold depending on the engine speed. In order to ensurereproducible and stable operation, the engine was run continuously for various minutesaccording to the mentioned knocking condition. For detailed information about theexperimental setup and procedure as well as the results, refer to Appendix B andBanzhaf (2016).

5.3.2 Analysis method

Two different analytical techniques are used in this study to address the detection andclassification of knock. Since selecting one detection method can lead to misdetec-tion of a knock event, the utilisation of k-means clustering and k-nearest neighboursresults in more accurate knock detection. Principal component analysis (PCA) as adimensionality reduction technique is used to visualise the data. It illustrates the databy reducing the complexity of the problem while keeping most of the information.

k-means is an unsupervised clustering algorithm that groups individual data pointsby assigning them to the closet centroid. The k-means algorithm partitions I obser-vations from a XI×J data matrix - where J is number of knock detection methods(variables) - into K clusters with CK×J centres by minimising the distance –in thiscase the Euclidean distance - between each observations and centres. The algorithm ofk-means clustering is as follow (Arthur and Vassilvitskii, 2007)

1. Initialise K centres randomly or from prior knowledge –assigned by user;

2. For each observation i ∈ {1, . . . , I} , find the squared Euclidean distance and,then, allocate it to the cluster k ∈ {1, . . . , K} where the Euclidean distance d2 isminimum;

(d2j)

(k) =J∑j=1

∥∥∥xi,j − c(k)j

∥∥∥2

(5.9)

where xi,j is a member of XI×J and c(k)j is a member of CK×J .

3. Set new centroids of c(k) by calculating the centre of the mass of all observationsin cluster K;

c(k) =1

nk

∑i∈k

xi,j (5.10)

where nk is the number of observations in cluster K.

4. Repeat steps 2 and 3 until the centroid c does not change.

The data from this method will be used to train the k-nearest-neighbours method,which is fast and non-parametric.


5.4. Selection of detection method 77

The k-nearest-neighbours (kNN) is a straightforward and effective pattern recog-nition method, which classifies input data based on the majority of the nearby pointsclass label. The nearest neighbours of a sample are the training data, which displaythe smallest distances. The Euclidean distance is generally used in the literature andit will be used in this study as well. The number of the nearest neighbours (k) is themain parameter needing to be set for this method. If k is set too large or too small, itcan lead to high bias or high variance in the estimation of the sample class. The basicalgorithm of kNN is as follows (Cunningham and Delany, 2007):

1. Add a new sample point;

2. Calculate distances;

3. Find the nearest neighbours based on k;

4. Classify the sample based on the density of the neighbours.

Principal component analysis is one of the popular techniques to reduce the di-mensions of the problem. It extracts the most important information of the data setin terms of principal components. Principal components are orthogonal to each otherand capture most of the variance from the original data set. So the first few principalcomponents can be chosen when they are showing the most variance in the data set.Principal components can be calculated using singular value decomposition, usingEquation 5.11 (Jackson, 2005).

X = YΣU′ (5.11)

where Y is left singular vectors matrix, U is right singular vectors matrix and isknown as a loading matrix. Σ is a diagonal matrix and contains the eigenvalues ofXXT.

5.4 Selection of detection method

One of the aims of this study is to find robust metrics of knock detection and intensityregardless of engine operating conditions and fuel type. Methods that are highlysensitive to the engine speed and fuel will not be transferable. Figure 5.2 showsthe correlation of the knock metrics with the engine speed and fuel type based onmore than 5,000 cycles of the engine from multiple operating points. In-cylinder peakpressure is highly correlated with fuel type. When the percentage of ethanol is higher,the available oxygen increases in the combustion chamber, which can result in morecomplete combustion and higher pressure. The pressure rise rate is anti-correlated



with speed and correlated with fuel type. At higher speed, the combustion duration isshorter and, hence, the pressure rises at lower rates. On the other hand, higher ethanolcontent of the fuel provides more oxygen and results in more rapid combustion, whichincreases MPRR. Heat release rate and its decay rate are anti-correlated and correlatedwith speed, respectively. Dimensionless knock indicator shows positive correlationwith both fuel and speed, and knock onset is anti-correlated with fuel type. Thisindicates that at lower percentages of ethanol, the knock onset advances comparedto higher ethanol concentration. This result shows that if the above indicators need tobe used as knock metrics, they need to be calibrated based on speed or fuel type.

PPPRR

MHRR

HRRD

MAPO M

EIM

PO IE DKIRM

S

MPRF

KO

% Ethanol

Speed

Figure 5.2: Pearson’s correlation of knock metrics with engine speed and fuel type.Blue colour shows positive correlation and red colour shows negative correlation. Thesize of circle shows the magnitude of correlation.

The filtered pressure derived indicators are less sensitive to fuel and speed, sincethey directly show the resonance in the chamber caused by auto-ignition. These indica-tors are MAPO, ME, IMPO, IE, RMS, and MPRF. The other methods that vary greatlywith fuel type and speed are not suitable to classify the knock intensity. Furthermore,since MAPO and IMPO are respectively proportional to ME and IE, the latter methodsare considered due to a lower sensitivity to noise. Thus, four methods - ME, IE, RMS,and MPRF - will be utilised simultaneously to detect and classify the knock intensity.The general control diagram demonstrating the utility of this study can be seen inFigure 5.3. The focus of this study is on detection and classification of knockingevents.


k-means clustering is utilised to cluster observations - engine cycles - from the testedengine in four groups based on the four knock metrics. Three hundred cycles werechosen to be used in k-means clustering for each engine speed and fuel type, that is,1800 cycles in total. This classification will be used to train k-nearest neighbours,



EnginePressure

transducer

Actuators such as spark timing, injection timing, throttle, EGR

valve

+

K-nearest-neighbours

classification

Band pass filter

HT

RMS

FFT

ME

IE

Reference pattern

obtained by k-means

MPRF

Engine control unit

Noise

Combustion event class

Feedback

Gas pedal

Figure 5.3: A control block diagram of engine with knock detection and classificationas a feedback

which can be used in real-time application. PCA here is used to reduce the dimensionsof the data for visualisation. Four knock metrics indicate four dimensions of the data.PCA is used to reduce these four to two principal components, which includes mostof the variance in the data. In this case, the first two principal components show 99%of the total variance. The observations - or each of the cycles - are shown in Figure5.4 based on these two principal components, while k-means clusters the observationbased on those four metrics.

Figure 5.4 shows the results based on k-mean clustering. Note that this approachis unsupervised clustering with an input from the user. MPRF and IE are located inone quartile, and RMS and ME are in another. The quantity of these four indicatorsis small for no-knocking events. Thus, no-knock events formed a dense cluster of thecycles on the left side of the graph. The knock intensity increases from the left to theright side of the graph. The k-means clusters show the intensity of knock. The redcolour indicates the intense knock, orange colour shows moderate knock, blue colourshows slight knocking and green colour shows no-knock events. This classificationis important as the engine control unit can detect knock and prevent intense knockingwhile allowing the engine to run on some extent of moderate and low knocking basedon the mode of operation and regularity of knocking. Now this classification can beused in a method that can run in real time to classify the knock. Furthermore, Figure5.4 shows that E25 contributes to more intense knock.



Figure 5.4: k-means clustering of the observation (Reference cycles). Red colourshows high intensity knock, orange colour moderate knock, blue colour slight knock,and green colour no-knock events.

The output of k-means is used as an input for a kNN classifier to differentiateknocking events cycle-by-cycle for real-time knock detection. This method is fast andcomputationally cheap. The k-means clustering was done based on 1800 cycles. Thesame 1800 cycles were used in kNN to determine the error of kNN using Equation5.12

Error =100

N

N∑n=1

f(Cn,kNN , Cn,kMean) (5.12)

f(CkNN , CkMean) =

0 if CkNN = CkMean

1 if CkNN 6= CkMean

where C is number that assigned to each cluster (1 = No knock, 2 = slight knock,3 = moderate knock, 4 = intense knock). N is the number of cycle which is 1800cycles in this case. To select an optimum value of nearest neighbours (k), the numberof nearest neighbours (k) is varied from 1 to 29 and the error of each k is calculatedusing Equation 5.12. The error is plotted against the number of nearest neighbours inFigure 5.5. As shown in the figure, in this case, considering k equal to one gives theleast error, which is 0.15%. By increasing k, the total error increases to 0.3%, which isstill acceptable, but the error in detection of moderate knock increases drastically and



reaches around 1.3%. Therefore, the number of nearest neighbours of one is consideredhere.

1 5 10 15 20 25Number of the nearest neighbours - k

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Err

or [%

]Total ErrorIntense knockModerate knockSlight knockNo-knock

Figure 5.5: Change in the error of knock detection of kNN by increasing the numberof neighbours (k)

To demonstrate the potential for real-time knock detection, Figure 5.6 shows ex-ample results from kNN together with some of the results from the reference patternby k-means clustering. A reduced number of observations is shown in Figure 5.6for clarity. kNN considers the smallest Euclidean distance between the neighboursof the sole observation to mark its class based on four metrics. For example, in thefour dimensions - each metric indicated by one dimension - the observation A iscloser to the observation B that is from the reference-pattern. Thus, the class of theobservation A is chosen as the class of B. In this way, the appropriate knocking eventcan be quickly identified for the relevant engine application. The same metrics andclassification methods are applicable for the accelerometer as well to detect knock.The reference training can be done for a small set of samples while the engine isrunning on a combination of knocking and non-knocking events for a short period oftime (a few minutes) and then it can be used as a reference for knocking detection inreal time.



Figure 5.6: a) kNN classification of the observation (Validation set) b) kNNclassification together with K-means. “A” and “B” shows an observation by kNN andk-means, respectively. Colours show the combustion event classes, which are chosenby the corresponding method.

5.6 Conclusion

Twelve knock metrics based on in-cylinder pressure trace were briefly reviewed, andfour metrics were identified that showed less dependency on engine speed and fueltype. The pressure signal was band-passed filtered between 3 kHz to 20 kHz to remove


5.6. Conclusion 83

the lower frequency content of the signal and to decrease higher frequency noise.Two of these methods, maximum of pressure oscillation envelope and integration ofpressure oscillation envelope, are derived from the Hilbert transform of the signal,which indicates the amplitude and the total energy of the signal over the period ofcombustion, respectively. MPRF is determined by discrete Fourier transform of thesignal and demonstrates the energy of the first resonance frequency of the combustionchamber. The last method is RMS of the signal which represents the average signalpower. These methods were simultaneously utilised in a k-means clustering techniqueto cluster the knocking events as a reference classification. This is then consideredas a source for k-nearest neighbours to detect and classify the knocking for eachengine cycle that can be utilised in real time. In the study, kNN was successful in theknock detection and classification for 99.8% of events. The method only needs a smallamount of computational time to make a decision and it is applicable for typical engineworking conditions. While this study only utilised the in-cylinder pressure signal, allthe metrics and methods are applicable to an accelerometer (knock sensor).



85

Chapter 6

Engine misfire detection

Detection of misfire in a six-cylinder diesel engine using acoustic emission signals

Published in:

Proceedings of the ASME 2018 International Mechanical Engineering Congress andExposition. https://doi.org/10.1115/IMECE2018-86506


Mohammad Jafari1,2, Pietro Borghesani3, Puneet Verma1,2, Ashkan Eslaminejad4, Zo-ran D. Ristovski1,2, Richard J. Brown1

1Biofuel Engine Research Facility (BERF) and 2International Laboratory of Air Qual-ity and Health (ILAQH), Queensland University of Technology, Brisbane, Queensland4000, Australia3School of Mechanical and Manufacturing Engineering, University of New SouthWales, Sydney, New South Wales 2052, Australia4Mechanical Engineering Department, North Dakota State University, Fargo, NorthDakota 58105, USA


86 Chapter 6. Engine misfire detection









87



experiments, preformed data analysis, and wrote themanuscript

SignaturePietro Borghesani Aided with data analysis and development of the paper,

and revised the manuscriptPuneet Verma Assisted with experiment, and revised the manuscriptAshkan Eslaminejad Aided with data analysis and revised the manuscriptZoran D. Ristovski Extensively revised the manuscriptRichard J. Brown Supervised the project, aided with data analysis and

extensively revised the manuscript




Name Signature Date





Abstract

This study will focus on the detection of misfire using acoustic emission sensor (AE) ina multi-cylinder diesel engine. Detection of misfire is important since this malfunctioncan cause the engine to stall in a short time. To investigate the misfire, an experimentalengine was run with and without injection of the fuel in the first cylinder. The acousticemission signal was acquired synchronously with the crank angle signal, in order tohave a reference for the transformation from time to angular domain. The AE signalwas then processed using the squared envelope spectrum (SES) to highlight angle-periodic modulations in the signal’s power (cyclic bursts). This study will present theeffectiveness of this combination of sensor technology and signal processing to detectmisfire in a six-cylinder diesel engine connected to a hydraulic dynamometer.

6.1 Introduction

Low fuel consumption and relatively long life time, compression ignition enginescan be found in different applications from small power generators in the farms toheavy-duty ship engines. More advanced CI engines are employed in the transporta-tion sector. Turbocharging, supercharging and common-rail fuel injection are amongtechnologies that enhanced the combustion quality and engine performance. On theother hand, through emerging the new technologies, monitoring engine operation isbecoming more important in order to control the performance, increase life-span andprevent the failure of engine. Among various causes of engine failure, misfire is morecommon and significant. It can result in stalling and poor performance of the engine.

Some researchers have studied the detection of misfire in CI engines using non-destructive methods. Generally these methods are divided to three main categories:

1. Engine exhaust pressure;

2. Instantaneous crank shaft speed;

3. Structure-borne vibration and acoustic emission based detection.

The engine exhaust pressure method achieved reasonable results as shown by Willimowskiand Isermann (2000) and Jiang et al. (2008); however, this method is not capable oflocalising the misfire. Instantaneous crankshaft speed monitoring method has gainedmore attention in the literature and industry, since crankshaft encoders have a low costand high durability (Liu et al., 2013, Williams, 1996). To identify misfiring events



in a multi-cylinder engine, an index based on CA angular velocity or accelerationhave been calculated and optimised for each cylinder based on cylinders firing orderinformation (Assaf et al., 2011, Merkisz et al., 2001, Wu and Lee, 1999). Thesemethods adjust the index using real-time and post processing approaches for eachengine operation. On the other hand, normalising the misfire detection indices is atedious task for all the engine operation zones and increases the cost of calibration(Guo, 2017). A few studies utilised accelerometers and showed successful results.Sharma et al. (2014) and Kawamura et al. (2004) made use of machine learning (ML)methods and accelerometer to detect misfire. Although these methods were successful,ML methods are required to be tuned for all the operation events, as well, for the real-time use.

Acoustic emission sensors are among the low-cost sensors and have an advantageof higher signal-to-noise ratio compared to accelerometers. According to British Stan-dard, Acoustic Emission is defined as “transient elastic waves generated by the releaseof energy within a material or by a process”. The structural acoustic emission methodhas been used for the health monitoring of the pump and bearings; however, this fieldis relatively new for the condition monitoring of IC engines.

Friis-Hausen and Fog (2001) presented a successful approach based on AE for thedetection of cylinder misfire and exhaust valve leaking in a two-stroke four-cylinderCI marine engine. They analysed signals using principal component analysis and clas-sified faults with the neural network technique. As a result, they reported the acquiredAE signals are more desirable than other sensors such as vibration. Nivesrangsanet al. (2005b, 2007b) investigated the acoustic emission mapping of two CI enginesfrom the wave propagation of the engine block. They used a nine AE sensor arrayaround the engine block to localise the source of AE and measure the attenuationfactor. Moreover, they developed velocity and energy based approach to find the AEsignal source location. These approaches were useful to identify single source signalsand multiple source signals, respectively. Wu et al. (2015b) presented a techniquebased on blind source separation and pencil lead break techniques to separate andnormalise the measured AE signal from a 4-cylinder diesel engine in order to overcomethe non-linearity of the signals. They showed that each cylinder can separately bemonitored using this technique. In a recent study, Dykas and Harris (2017b) utilisedthe synchronous average of the root mean square (RMS) of AE signals for enginecondition monitoring purposes. They placed four AE sensors on the different partsof a single cylinder diesel engine and found that AE signals are highly variable ofthe engine operation like speed, load and, also, injector fault. All these techniquesshowed that AE sensor is effective for diesel engine misfire detection; however, theyrequired multiple sensors and a large number of engine cycles to develop and tune their



methods. In this study, a novel method is proposed to detect misfire based on 10 enginecycles using single AE sensor.

Analysing the engine pressure AE signal in time domain is challenging due to thefluctuation of engine speed that makes the signal quasi-periodic Lin and Tan (2011b).Indeed, the objective is to find a relation between the engine signals and kinematics,which is dominated by the engine cycle and crank angle position. Hence, these signalsare better defined in the angle-domain rather than time-domain (Antoni et al., 2002a).In this study, the analysis will be mainly done in the crank angle-domain by employingthe crank angle encoder as a reference for order-tracking.

This study will focus on the detection of misfire using AE sensor in a turbochargedsix-cylinder diesel engine. In order to investigate the misfire, an experimental enginewas run with and without injection of the fuel in the first cylinder. The AE signal wasacquired synchronously with the crank angle signal, in order to have a reference for thetransformation from time to angular domain. The AE signal was then processed usingthe squared envelope spectrum to highlight angle-periodic modulations in the signalpower (cyclic bursts). This study will present the effectiveness of this combination ofsensor technology and signal processing technique to detect misfire in a multi-cylinderdiesel engine.

6.2 Test rigs and instrumentation

This study was conducted in the Biofuel Engine Research Facility (BERF) at Queens-land University of Technology (QUT). A 6-cylinder turbocharged diesel engine withcommon-rail system was employed in this research. The specifications of the engineare shown in Table 6.1. The engine was controlled by a hydraulic brake dynamometer.

Table 6.1: Tested engine specificationSpecifications

Model Cummins ISBe220 31Number of cylinders 6 in-lineCapacity (L) 5.9Bore×stroke (mm×mm) 102 × 120Max. power (kW/rpm) 162/2500Max. torque (Nm/rpm) 820/1500Compression ratio 17.3:1Aspiration Turbo-charged & after cooledFuel injection Common-Rail

This research engine included an in-cylinder pressure transducer and a crank angle


6.2. Test rigs and instrumentation 91

encoder. Other sensors were also mounted on these engines showing the fuel consump-tion, charge air flow, and exhaust temperature. The pressure signal, injection signal andcrank angle signal are acquired by a National Instrument data acquisition board (DAQ).An AE sensor was attached to the engine block surface and was connected to the sameDAQ. The data were collected using LabVIEW® software. To ensure the acquisitionof the full AE sensor frequency band, the data were acquired in the rate of one millionsamples per second in this study. The schematic of the test rig is shown in Figure 6.1.

1 2 3 4 5 6

PressureAE

DAQ

CA

PC

Figure 6.1: Test setup

The pressure, crank angle and AE sensors were mainly employed for this exper-imental study. The pressure sensor that was installed in the Cummins engine firstcylinder was a Kistler piezoelectric transducer (6053CC60). This sensor had a reason-able stability at high temperatures and low thermal shock errors that made it suitablefor working in the cylinder. The crank angle on the Cummins was measured by aKistler crank angle encoder (type 2614) which had a resolution of 0.5 degrees. Thedata from this sensor was digital data that is acquired synchronously with the pressureand AE signals (analogue signals). The AE sensor was mounted on the Cumminsengine head next to the first cylinder as shown in Figure 6.1. The AE sensor was ageneral purpose Physical Acoustics R15α sensor with a frequency range of 50 kHzto 400 kHz. As displayed by the manufacturer, this multi-purpose sensor provides anacceptable combination of high sensitivity and low-frequency rejection.



6.3 Analysis techniques

Random signals are categorised as stationary or non-stationary signals. If a statisticalproperty of a random signal is constant, it is called stationary signal. Most signalsgenerated by mechanical component are non-stationary. There is a subsection of non-stationary signals that have a periodic statistical property. These signals are identifiedto have a cyclostationary property. The periodicity of these signals can be determinedin the time or angular domains. Generally, the vibration behaviour of rotating machin-ery is mostly classified as angle-cyclostationary because they operate periodically inthe angular domain (Antoni et al., 2002a). The envelope analysis, which is used inthis study, can be classified as a tool of cyclostationary analysis, identifies periodicfluctuations in the signal power (i.e. variance) (Borghesani et al., 2013).

To detect the misfire in the engine, AE data, which was acquired for five seconds,was used for the diagnosis. The signal is band-pass filtered based on a resonantfrequency of the sensor. Then, the envelope of the signal is determined using Hilberttransformation. The discrete Fourier transform (DFT) of the squared envelope is takento represent the spectrum in Frequency domain. This is called as the squared envelopespectrum (SES). The difference in the magnitude of DFT can indicate the misfire bycomparing the misfiring signal and normal signal of engine. Then, to localise themisfiring cylinder, the procedure below is followed (Borghesani et al., 2013)

1. Order tracking of the signal, to remove the shaft speed fluctuation;

2. Discretise the engine cycle based on the CA and top dead centre;

3. Take the synchronous average of the AE signal as first order cyclostationary.

Thus, the misfiring cylinder can be identified by comparing the final result with thenormal operation of the engine. The process flow chart is shown in Figure 6.2.

6.4 Results

In the experiment, six speeds with low load and two speeds with full load on the enginewere considered to study the capability of squared envelope spectrum (SES) for variousengine performances. The injection to the first cylinder was turned off at each step tosimulate the misfire in the engine. Since the AE signal of multi-cylinder engines arecomplex due to many combustion events and valves opening/closure in a short time,it is important to know where the combustion events can be seen in a crank angle.


6.4. Results 93

Band-pass Filter

Envelope

DFT

Square

Squared Envelope

Spectrum

Sync Average

Order tracking

Compare to Normal

Operation

AE Signal

CA Signal

Figure 6.2: Process flow chart

This information is usually provided by the manufacturer. Also, to become certainabout the events, the in-cylinder pressure from the first cylinder was synchronouslyacquired with AE and CA signals. Figure 6.3 shows the AE and raw pressure signalfor one cycle of the engine at 2000 RPM, with both injection on and off in that cylinder.The blue line shows the normal engine operation signal, and the red line indicates thefirst cylinder injector is off. The first peak of normal pressure (in blue) is related tomotoring pressure and the second peak is the combustion peak. As it is evident fromthe figure, it is difficult to determine misfire from AE raw signal. Hence, SES will beused to have more clear representation and easily detect the misfire. The AE data ofFive consecutive cycles was used in this analysis.

0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600 630 660 690 720

-10

0

10

AE

Sen

sor

- V

Injector onInjector off

0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600 630 660 690 720Crank Angle - Degree

0

2000

4000

6000

Pre

ssur

e -

kPa

Combustion Order Cylinder 6 Cylinder 2 Cylinder 4 Cylinder 1 Cylinder 5 Cylinder 3

Figure 6.3: Raw AE and pressure signals of one cycle



Since misfire causes unbalanced forces on the crank shaft, the engine may showunstable behaviour, which should be noticeable in the AE or vibration signals. SESversus event frequencies of AE signal are considered to investigate this difference.Each event in the engine has its own frequencies based on the rotation of the shaft, forexample, in a six-cylinder engine at 2000 RPM, the frequency of shaft rotation is 33.34Hz, one complete cycle is 16.67 Hz and combustion in cylinders is around 100 Hz. Asshown in Figure 6.4, when the engine is running on normal operation (with all thecylinders working), it is completely stable and the envelope spectrum of the shaft andcombustion frequency are smooth. On the other hand, when the engine operates withmisfire, it shows smaller peak on the second and sixth frequency order (shown witharrows in Figure 6.4). The difference between max SES peak at shaft and combustionfrequency of different engine speeds had been performed to investigate the capabilityof this method. Figure 6.5 shows the SES peaks of the second and sixth order. Thesecond peaks, related to normal operation, are higher in most of cases except for 2400rpm. The sixth peaks, which are associated with the combustion, are more distinct andhigher all the time for normal operation compared to the misfire operation.

10 20 30 40 50 60 70 80 90 100 110 120

Frequency - Hz

0

0.2

0.4

0.6

0.8

1

1.2

1.4

SE

S

Injector OnInjector Off

Figure 6.4: Squared envelope spectrum (SES) 33.33 shaft rotation frequency 99.9combustion frequency

To detect the misfiring cylinder, the absolute envelope of the signal is used tocompare with normal operation as shown in Figure 6.6. The misfire AE envelopehas lower magnitude during the first cylinder combustion. Since the AE sensor wasconnected close to the first cylinder, this cylinder AE signature was more apparentcompared to other cylinders. Thus, using two sensors or an optimised place can helpto improve the accuracy of this method.


6.5. Conclusion 95

1200-LL 1500-LL 1500-HL 1700-LL 2000-LL 2000-HL 2100-LL 2400-LL0

0.5

1

1.5

SE

S P

eak

(In

j On)

0

0.5

1

1.5

SE

S P

eak

(In

j Off)

1200-LL 1500-LL 1500-HL 1700-LL 2000-LL 2000-HL 2100-LL 2400-LLSpeed - RPM

0

0.5

1

1.5

SE

S P

eak

(In

j On)

0

0.5

1

1.5

SE

S P

eak

(In

j Off)SES 6th peak - Combustion Frequency

SES 2nd peak - Shaft Frequency

Figure 6.5: Comparison of SES Peaks (LL - Low load, HL – High load)

0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600 630 660 690 720

Crank Angle - Degree

0

1

2

3

4

5

6

7

8

9

10

Sig

nal E

nvel

ope

Injection onInjection off

Combustion in Cylinder 1

Figure 6.6: Synchronous average of AE signal Envelope in crank angle domain

6.5 Conclusion

There has been a growing interest in the development of non-intrusive techniques tomonitor machines. Vibration and acoustic emission (AE) monitoring are among bothlow cost and applicable techniques. The complexity of such signals in CI engineshowever makes it difficult to identify key diagnostic information. Thus a targeted sig-nal processing method, combined with a powerful sensor technology is required. AEsignals have the advantage of higher signal to noise ratio compared with accelerometersignals. Acoustic emission sensors have been generally used mostly for structuralhealth monitoring, with limited applications to rotating machines (e.g. pumps andbearings) and a very small number of works on compression ignition engines. Hence,



this research illustrated the capability of using acoustic emission sensor and crankangle encoder with a straight forward signal processing method to successfully detectthe misfire in the turbocharged six-cylinder CI engine. The squared envelope spectrumwas shown to highlight the difference between the normal and misfiring operationof the engine. The synchronous average of signal envelope over a short period time(five consecutive cycles) revealed the misfiring cylinder, which can be informativeto control the performance of an IC engine. This study can be further developed tomonitor the engine misfire using acoustic emission in real time using machine learningtechniques such as k-nearest neighbour. The application of AE sensors is not limited tomisfire detection. Hence, the combination of AE sensor and CA encoder can providea powerful tool for the engine monitoring and diagnosis purposes.


97

Chapter 7

Conclusions

This research investigation provides greater understanding of engine operation throughthe application of in-cylinder pressure and structure-borne acoustic emissions, togetherwith advanced data analysis. In addition to the introduction, literature review andconclusion, the body of this thesis consists of four novel research studies in chapterform. Each chapter was published or submitted to high-ranking peer-reviewed jour-nals. In Chapter 3, a comprehensive study was conducted to show the relationshipbetween in-cylinder pressure, acoustic emissions, and 38 engine performance andexhaust emission parameters using principal component analysis (PCA). The PCAgives justification for further investigations of acoustic emissions as a diagnostic toolfor engine performance and emission parameters. Therefore, in Chapter4, an algorithmbased on complex cepstrum analysis and neural networks was developed to reconstructin-cylinder pressure using acoustic emissions. Then, in Chapter 5, in-cylinder param-eters were used to diagnose engine knock by a new technique to classify and intensifythe knocking event. Chapter 6 addressed another important engine fault - misfire -using structure-borne acoustic emission signals and envelope analysis.

7.1 Results summary and conclusion

Chapter 3 provided a comprehensive study on forty internal combustion engine perfor-mance and emission parameters by using correlation analysis, PCA and hierarchicalclustering. Diesel, biodiesel and triacetin were utilised to investigate the effect offuel properties. While most of the study focused on just a few engine performanceor emission parameters, this study considered a wide range of parameters from sixmain groups: engine performance, in-cylinder derived data, structure-borne acousticemission (AE), fuel properties, exhaust emission, and particle morphology and nanos-tructure. AE was used to investigate the feasibility of such sensors to monitor engine


98 Chapter 7. Conclusions

performance and emissions. The engine emission was focused on NOx and particu-late matter. The chemical composition and physical properties of particle emissionsprovided useful information on oxidation reactivity of particles using oxygenated fuel.

This study presented a strong correlation between AE signal indicators (the rootmean square and the maximum envelope of the signal), engine performance parame-ters, peak pressure (PP), indicated work (IW), and indicated mean effective pressure(IMEP). Therefore, an AE sensor is capable of acquiring a signal that can be utilisedfor engine health monitoring and diagnostics.

The in-cylinder parameters, such as PP, IW and IMEP, were correlated with engineperformance parameters and thermal efficiency, and were anti-correlated with brakespecific NOx. An increase in maximum pressure rise rate (PRRmax) led to increasedprimary particle diameter, particle fringe length and NOx. Longer ignition delay andhigher engine speeds can increase the nucleation particle emissions. Overall, theseresults showed the potential of in-cylinder parameters to monitor exhaust emissions.

While the importance of in-cylinder pressure transducers to monitor engine per-formance and exhaust emissions was shown in 2 and 3, these sensors are not beingcommercially used due to their high price. Hence, Chapter 4 investigated the recon-struction of in-cylinder pressure trace using AE sensors, as the PCA results indicateda strong correlation between these two sensors. Using a combination of complexcepstrum analysis and neural networks, an algorithm was developed to successfullyreconstruct the pressure that can be applied to various engine operations and fuel types.Furthermore, the reconstructed pressure was utilised to determine some in-cylinderparameters such as PP, PP timing, IMEP and PRRmax. The AE reconstructed pressurecan measure PP, PP timing with small error, and estimated IMEP within an acceptablerange. However, it was not successful in measuring PRRmax due to noise.

In Chapter 5, the in-cylinder pressure parameters were utilised to monitor eachengine cycle and to diagnose knock. Twelve knock metrics were investigated and fourof them were chosen which showed less sensitivity to engine operation mode and fueltype. These four knock metrics were used simultaneously to detect and classify theknocking events. Utilising multiple knock metrics improved the capability of knockdetection, which is useful in the pursuit of more efficient and reliable engines. Knockclassification can provide more information for a flexible knock control strategy thatcan lead to the desired engine damage prevention, while decreasing fuel consumption.

Chapter 6 presented the utilisation of AE sensors to diagnose misfire without hav-ing knowledge of the in-cylinder pressure. A range of engine speeds and loads wereconsidered to evaluate the effectiveness of AE to detect misfire in a modern six-cylinder


7.2. Recommendations for the future 99

engine. The squared envelope spectrum (SES) of the AE signal showed a differencebetween normal and misfiring operation of the engine. SES for the normal operationhad higher peaks compared to the misfiring operation. Furthermore, the synchronousaverage of the signal envelope over a short period of time is able to identify themisfiring cylinder.

This dissertation advances knowledge of engine health monitoring and fault di-agnosis. In-cylinder pressure transducers and acoustic emission sensors are utilisedwith advanced statistical and analytical techniques that can provide a powerful toolto provide information useful in the pursuit of more efficient and reliable engines.This research provides novel approaches to successfully employ AE sensors to providepractical information for monitoring IC engines.

7.2 Recommendations for the future

This thesis offers practical means for using a relatively low cost sensor for IC enginemonitoring and diagnostics. With regards to strengthening the study featured here,the most practical recommendation would be to apply an AE sensor with the providedtechniques in real time. Designing a closed loop engine management unit to effectivelycontrol the engine performance and its combustion is feasible using these techniquesfor a feedback.

In the PCA analysis, the in-cylinder parameters and AE indicators showed correla-tion or anti-correlation with some of the engine exhaust emissions. Future work couldinclude an empirical or semi-empirical model of the system based on the statistical andphysical relation between in-cylinder parameters and AE indicators to predict exhaustemission such as NOx. The dimensionally reduced data set generated by PCA can beutilised to build a model using statistical modelling tools, such as principal componentregression.

This study mainly used coconut oil biodiesel and butanol; however, engine perfor-mance and exhaust emissions using biofuels from different feedstocks can be studiedby using the same analysis techniques as have been presented here. This can show thedifference in performance of different types of biofuels.



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123

Appendix A

Supplementary data for PCA analysis

Table A.1: Engine performance parameters

Fuel Engine condition

Brake Power [kW]

Torque [Nm]

Fuel flow rate [l/min]

Injection pressure

[MPa]

Exhaust Temperature

[oC]

Boost pressure [kPa]

Brake thermal efficiency [%]

Air–fuel equivalence

ratio

M1 SD2 M SD M SD M SD M SD M SD M SD M SD

Die

sel

S1500 L253 31.10 0.45 198.15 2.46 0.14 0.01 45.97 0.20 323.10 2.44 121.00 0.00 36.41 0.53 1.77 0.02

S1500 L50 64.97 0.48 414.05 2.97 0.28 0.02 62.77 0.42 434.81 2.44 150.99 0.15 38.47 0.28 1.07 0.01

S1500 L75 95.29 0.69 607.03 3.65 0.42 0.03 74.93 0.55 505.22 2.37 189.29 0.70 37.91 0.28 0.87 0.01

S1500 L100 126.77 1.02 807.66 6.10 0.57 0.04 75.60 0.65 562.55 1.86 228.98 0.76 36.95 0.30 0.75 0.00

S1800 L100 149.40 1.22 793.00 6.23 0.63 0.01 88.17 0.55 527.23 2.80 266.30 0.96 39.07 0.32 0.91 0.01

S2000 L100 156.25 1.29 747.20 6.25 0.67 0.01 97.00 0.91 530.21 2.90 268.90 1.48 38.50 0.32 0.95 0.01

B2

0

S1500 L25 30.06 0.42 191.26 2.40 0.14 0.01 45.98 0.16 316.65 2.19 120.73 0.69 36.21 0.51 1.81 0.02

S1500 L50 63.51 0.72 403.92 4.05 0.28 0.02 62.85 0.40 430.12 2.27 149.00 0.00 38.34 0.43 1.08 0.01

S1500 L75 93.81 0.76 596.82 4.10 0.42 0.03 74.97 0.61 501.20 2.71 187.00 0.00 37.66 0.30 0.87 0.01

S1500 L100 124.81 1.02 793.70 6.03 0.56 0.04 75.68 0.58 556.72 2.42 225.18 1.03 37.95 0.31 0.78 0.01

S1800 L100 146.83 1.08 780.24 5.54 0.63 0.01 88.24 0.55 519.39 2.81 263.39 1.01 39.26 0.29 0.93 0.01

S2000 L100 153.98 1.08 734.65 5.07 0.67 0.01 97.36 1.05 517.46 3.01 268.80 1.64 38.67 0.27 0.98 0.01

B5

0

S1500 L25 28.57 0.51 181.31 2.45 0.14 0.01 46.12 0.35 305.39 1.80 119.00 0.00 35.14 0.63 1.84 0.02

S1500 L50 59.89 0.62 381.06 3.65 0.28 0.02 62.79 0.45 413.00 2.88 144.95 0.31 37.37 0.39 1.09 0.01

S1500 L75 88.88 0.67 565.35 3.60 0.42 0.03 74.98 0.61 486.42 2.76 180.09 1.00 36.92 0.28 0.88 0.01

S1500 L100 117.81 0.91 749.15 5.35 0.56 0.04 75.66 0.66 540.42 2.35 215.03 0.92 36.87 0.28 0.77 0.01

S1800 L100 139.12 1.14 737.67 5.69 0.63 0.01 88.17 0.55 501.67 2.65 253.60 1.00 38.42 0.32 0.93 0.01

S2000 L100 146.28 1.20 699.00 5.57 0.67 0.01 97.14 1.00 492.62 2.60 266.25 1.23 38.02 0.31 1.01 0.01

Bio

die

sel

S1500 L25 24.81 0.48 157.67 2.64 0.14 0.01 45.99 0.16 283.99 1.69 117.00 0.00 32.74 0.64 2.01 0.02

S1500 L50 54.65 0.67 347.27 4.34 0.28 0.02 62.85 0.36 395.59 1.89 139.00 0.00 35.98 0.44 1.14 0.01

S1500 L75 80.76 0.65 513.10 3.67 0.42 0.03 75.05 0.41 472.19 2.97 168.87 0.49 35.53 0.28 0.89 0.01

S1500 L100 109.38 0.95 694.67 5.41 0.54 0.04 95.86 0.61 514.46 2.81 194.68 0.73 37.52 0.32 0.79 0.01

S1800 L100 127.38 0.92 675.84 4.54 0.63 0.01 88.33 0.59 481.94 2.70 236.85 0.57 37.29 0.27 0.95 0.01

S2000 L100 134.11 1.17 640.48 5.28 0.67 0.01 97.16 1.06 463.81 2.39 257.86 1.44 36.96 0.32 1.07 0.01

B9

6T

a4

S1500 L25 24.46 0.52 155.73 2.53 0.14 0.01 45.95 0.21 277.02 2.54 117.00 0.00 32.82 0.70 2.03 0.02

S1500 L50 53.72 0.66 341.63 3.72 0.28 0.02 62.87 0.34 388.51 2.48 137.26 0.67 36.13 0.44 1.15 0.01

S1500 L75 79.29 0.68 504.62 3.87 0.42 0.03 74.94 0.51 461.84 2.69 166.98 0.22 35.64 0.31 0.90 0.01

S1500 L100 103.79 0.90 659.49 5.13 0.52 0.03 97.50 0.63 500.74 3.42 187.99 1.00 37.47 0.32 0.80 0.01

S1800 L100 125.26 1.10 664.45 5.64 0.63 0.01 88.27 0.57 476.68 2.35 234.37 0.97 37.33 0.33 0.95 0.01

S2000 L100 132.09 1.20 630.98 5.48 0.67 0.01 97.35 1.06 458.67 2.32 254.83 1.48 36.99 0.34 1.07 0.01

B9

0T

a10

S1500 L25 23.87 0.42 152.15 2.32 0.14 0.01 45.96 0.22 274.73 2.38 116.95 0.31 32.98 0.58 2.04 0.02

S1500 L50 52.58 0.59 335.43 3.38 0.28 0.02 62.80 0.40 381.61 2.57 137.00 0.00 36.22 0.41 1.16 0.01

S1500 L75 78.41 0.63 499.73 3.59 0.42 0.03 74.84 0.57 458.16 2.97 165.06 0.35 35.85 0.29 0.90 0.01

S1500 L100 97.08 0.96 618.65 5.37 0.50 0.03 99.40 0.58 486.43 3.84 180.60 0.80 37.44 0.37 0.81 0.01

S1800 L100 122.41 1.06 649.90 5.50 0.63 0.01 88.22 0.51 470.49 2.79 231.04 0.77 37.28 0.32 0.95 0.01

S2000 L100 128.97 1.14 614.62 5.36 0.68 0.02 97.40 0.78 453.69 2.42 251.00 1.43 36.85 0.32 1.07 0.01

1M = Mean value 2SD = Standard deviation 3S = Speed, L = Load


124 Appendix A. Supplementary data for PCA analysis

Table A.2: Engine in-cylinder derived parameter


Indicated work

[kJ]

Indicated mean effective

pressure [kPa]

Peak pressure [kPa]

Start of injection

[°ca]

Start of combustion

[°ca]

Ignition Delay [°ca]

Maximum rate of pressure rise

kPa/ °ca

M1 SD2 M SD M SD M SD M SD M SD M SD

Die

sel

S1500 L253 0.521 0.004 531.6 4.0 5517 27 361.66 0.01 363.48 0.25 1.82 0.25 451.75 106.83

S1500 L50 0.955 0.004 974.4 4.4 6927 18 362.18 0.04 363.94 0.32 1.76 0.32 560.69 137.56

S1500 L75 1.339 0.006 1365.6 6.6 8804 21 364.31 0.05 365.98 0.30 1.67 0.30 534.11 131.59

S1500 L100 1.738 0.011 1772.7 10.8 10668 56 362.55 0.05 364.18 0.33 1.63 0.32 484.00 111.52

S1800 L100 1.720 0.011 1754.2 10.9 12437 38 360.08 0.05 362.11 0.44 2.03 0.44 426.58 46.59

S2000 L100 1.633 0.010 1665.1 10.5 12551 24 359.61 0.04 362.64 0.06 3.02 0.04 447.12 63.43

B2

0

S1500 L25 0.503 0.004 513.2 4.2 5492 38 361.67 0.01 363.51 0.27 1.83 0.27 420.86 102.97

S1500 L50 0.937 0.004 956.1 4.5 6861 38 362.15 0.04 363.87 0.31 1.71 0.31 531.85 128.50

S1500 L75 1.320 0.006 1346.7 6.4 8687 24 364.27 0.05 365.92 0.32 1.64 0.32 514.92 122.22

S1500 L100 1.707 0.043 1740.4 43.9 10433 349 362.51 0.97 364.15 1.06 1.64 0.33 473.60 295.39

S1800 L100 1.695 0.012 1728.6 12.0 12284 50 360.10 0.05 362.14 0.42 2.04 0.42 460.73 74.16

S2000 L100 1.603 0.013 1635.1 12.9 12545 21 359.59 0.04 361.99 0.45 2.39 0.45 430.84 49.47

B5

0

S1500 L25 0.480 0.003 489.3 3.3 5419 18 361.68 0.01 363.50 0.25 1.82 0.25 369.91 84.43

S1500 L50 0.890 0.004 908.0 4.2 6646 18 362.15 0.04 363.87 0.29 1.72 0.29 486.79 107.83

S1500 L75 1.259 0.006 1284.4 5.8 8361 22 364.26 0.05 365.92 0.32 1.66 0.32 474.20 115.20

S1500 L100 1.632 0.009 1664.6 9.0 10061 43 362.53 0.06 364.21 0.34 1.68 0.33 461.01 103.67

S1800 L100 1.591 0.175 1622.1 178.6 11603 846 359.95 1.12 361.99 1.26 2.06 0.42 424.98 80.28

S2000 L100 1.633 0.010 1665.1 10.5 12551 24 359.61 0.04 362.37 0.13 2.75 0.13 447.12 63.43

Bio

die

sel

S1500 L25 0.435 0.003 443.8 2.9 5371 28 361.68 0.01 363.51 0.25 1.83 0.25 310.79 73.25

S1500 L50 0.818 0.009 834.2 9.4 6407 36 362.00 0.05 364.26 0.07 2.27 0.04 490.87 109.91

S1500 L75 1.155 0.005 1177.9 4.8 7828 17 364.27 0.03 365.96 0.32 1.70 0.32 454.55 97.27

S1500 L100 1.435 0.080 1463.5 81.4 8739 465 363.08 1.02 364.86 1.13 1.77 0.30 646.76 136.88

S1800 L100 1.494 0.008 1523.4 8.5 11071 39 360.07 0.03 362.21 0.38 2.14 0.37 382.80 48.69

S2000 L100 1.423 0.064 1451.6 65.6 11993 267 359.58 0.59 362.31 0.68 2.73 0.21 399.22 29.39

B9

6T

a4

S1500 L25 0.428 0.003 436.8 2.9 5359 35 361.68 0.01 363.98 0.02 2.30 0.02 326.41 76.41

S1500 L50 0.812 0.003 827.6 3.3 6331 16 362.15 0.05 364.43 0.05 2.28 0.02 511.10 107.35

S1500 L75 1.141 0.004 1163.5 4.3 7731 15 364.27 0.03 365.96 0.31 1.68 0.31 467.33 107.09

S1500 L100 1.449 0.008 1477.6 8.2 8871 66 363.05 0.07 364.75 0.33 1.70 0.33 682.66 136.60

S1800 L100 1.472 0.008 1501.3 8.2 10930 57 360.07 0.04 362.14 0.38 2.07 0.38 386.40 55.54

S2000 L100 1.406 0.008 1433.9 8.3 11845 56 359.59 0.04 362.20 0.31 2.61 0.31 399.60 37.15

B9

0T

a10

S1500 L25 0.789 0.003 805.1 3.2 6182 41 362.05 0.07 363.77 0.28 1.72 0.27 515.25 109.70

S1500 L50 0.422 0.003 430.4 2.7 5304 24 361.67 0.01 363.51 0.26 1.84 0.26 345.33 86.27

S1500 L75 1.125 0.004 1147.2 4.2 7640 16 364.30 0.05 365.98 0.32 1.68 0.32 498.35 111.94

S1500 L100 1.336 0.036 1362.0 37.0 8269 214 363.57 0.06 365.26 0.31 1.68 0.30 713.42 129.68

S1800 L100 1.444 0.008 1472.8 8.1 10732 81 360.09 0.04 362.14 0.38 2.06 0.37 395.74 62.15

S2000 L100 1.375 0.008 1402.3 8.5 11645 63 359.57 0.04 362.07 0.41 2.50 0.41 393.45 32.94



125

Table A.3: Acoustic emission indicators and regulated emission parameters


AE Signal maximum

envelope

[V]

Brake specific fuel

consumption

[g/kWh]

Brake specific CO2

[g/kWh]

Brake specific NOx

[g/kWh]

Particulate matter

[g/kWh]

M1 SD2 M SD M SD M SD M SD

Die

sel

S1500 L253 0.63 0.23 228.9 14.9 589.0 29.9 4.01 0.26 0.1087 0.0048

S1500 L50 0.72 0.27 216.6 14.4 488.4 24.1 3.63 0.16 0.0609 0.0025

S1500 L75 0.78 0.27 219.8 14.8 463.4 22.0 3.44 0.17 0.1281 0.0072

S1500 L100 0.86 0.30 225.5 13.9 450.7 19.5 3.10 0.05 0.0662 0.0027

S1800 L100 1.23 0.34 213.3 2.8 434.6 4.1 2.95 0.06 0.0451 0.0014

S2000 L100 1.15 0.32 216.4 3.2 439.4 5.3 2.71 0.06 0.0562 0.0033

B2

0

S1500 L25 0.59 0.19 236.4 15.3 584.5 29.4 4.15 0.35 0.1083 0.0043

S1500 L50 0.71 0.27 223.3 14.7 486.1 23.7 3.99 0.24 0.0565 0.0022

S1500 L75 0.73 0.27 227.3 15.0 462.1 21.8 3.77 0.20 0.1284 0.0055

S1500 L100 0.90 0.31 225.6 15.0 438.5 20.3 3.41 0.05 0.0663 0.0031

S1800 L100 1.08 0.35 218.0 3.0 430.1 4.5 3.25 0.07 0.0461 0.0014

S2000 L100 1.10 0.32 221.4 2.2 437.3 3.9 2.98 0.06 0.0496 0.0021

B5

0

S1500 L25 0.59 0.18 253.8 17.1 604.3 31.6 4.18 0.29 0.0587 0.0017

S1500 L50 0.60 0.23 238.7 15.9 500.4 24.6 3.73 0.16 0.0256 0.0008

S1500 L75 0.78 0.29 241.6 15.9 473.3 22.1 3.47 0.15 0.0506 0.0024

S1500 L100 0.99 0.32 241.9 16.0 451.3 20.5 3.35 0.06 0.0328 0.0015

S1800 L100 0.80 0.34 232.2 3.9 444.1 5.5 3.23 0.06 0.0237 0.0008

S2000 L100 1.15 0.32 234.6 2.3 452.2 3.8 3.04 0.07 0.0250 0.0011

Bio

die

sel

S1500 L25 0.61 0.18 292.5 19.4 666.0 34.4 4.13 0.33 0.0148 0.0006

S1500 L50 0.59 0.22 266.1 17.4 530.4 25.9 4.34 0.23 0.0058 0.0003

S1500 L75 0.67 0.24 269.5 17.7 496.2 23.2 4.09 0.25 0.0094 0.0006

S1500 L100 0.78 0.28 255.2 17.0 451.0 20.5 4.96 0.15 0.0040 0.0004

S1800 L100 0.78 0.33 256.8 3.4 461.3 4.2 3.54 0.12 0.0074 0.0003

S2000 L100 0.84 0.33 259.1 2.6 470.6 4.2 3.30 0.12 0.0064 0.0005

B9

6T

a4

S1500 L25 0.61 0.18 300.6 19.5 671.5 33.8 4.15 0.22 0.0135 0.0008

S1500 L50 0.61 0.80 273.1 18.0 534.4 26.3 4.07 0.22 0.0056 0.0003

S1500 L75 0.71 0.25 276.8 18.4 500.7 23.4 4.19 0.18 0.0080 0.0005

S1500 L100 1.00 0.30 263.3 17.5 457.8 21.1 4.86 0.11 0.0072 0.0006

S1800 L100 0.94 0.37 264.3 2.4 465.9 3.0 3.64 0.11 0.0068 0.0003

S2000 L100 0.94 0.35 266.7 3.2 476.6 4.8 3.35 0.12 0.0055 0.0004

B9

0T

a10

S1500 L25 0.61 0.22 312.9 21.1 657.1 34.6 4.18 0.41 0.0090 0.0004

S1500 L50 0.59 0.19 285.0 18.8 526.4 25.9 4.36 0.26 0.0040 0.0002

S1500 L75 0.74 0.27 287.9 19.0 495.6 23.2 4.10 0.23 0.0053 0.0003

S1500 L100 1.01 0.32 275.7 18.5 458.1 21.1 5.01 0.12 0.0013 0.0001

S1800 L100 1.01 0.39 276.8 4.7 464.7 5.4 3.42 0.08 0.0045 0.0003

S2000 L100 0.94 0.34 280.1 6.4 475.9 8.5 3.18 0.09 0.0035 0.0003




Table A.4: Particle number


Particle number

concentration

[#/kWh] × 1013

Accumulation mode PN

[#/kWh]

× 1013

Accumulation mode count

median

diameter [nm]

Nucleation mode PN

[#/kWh]

× 1013

Nucleation mode count

median

diameter [nm]

M1 SD2 M SD M SD M SD M SD

Die

sel

S1500 L253 9.70 0.27 6.14 0.22 91.22 1.79 1.29 0.10 25.01 0.92

S1500 L50 5.38 0.19 3.27 0.11 91.80 1.17 0.62 0.07 22.03 0.66

S1500 L75 8.17 0.25 4.49 0.14 108.88 1.25 1.03 0.04 23.42 0.50

S1500 L100 4.63 0.14 2.44 0.07 106.94 0.76 0.72 0.04 22.51 0.47

S1800 L100 4.32 0.11 2.34 0.06 93.06 0.73 0.75 0.04 19.83 0.35

S2000 L100 5.32 0.21 2.95 0.22 89.42 3.56 0.87 0.06 19.52 0.64

B2

0

S1500 L25 11.08 0.24 7.23 0.14 84.25 1.11 0.03 0.19 17.88 0.93

S1500 L50 6.39 0.22 4.01 0.14 82.26 1.00 0.07 0.24 18.26 1.05

S1500 L75 9.10 0.26 4.75 0.14 106.72 0.97 1.30 0.06 21.78 0.64

S1500 L100 4.94 0.16 2.44 0.07 106.98 1.61 0.85 0.08 22.12 0.87

S1800 L100 4.66 0.10 2.48 0.07 91.82 1.24 0.82 0.11 19.75 0.53

S2000 L100 5.77 0.22 3.28 0.14 84.51 0.94 0.87 0.05 18.38 0.32

B5

0

S1500 L25 7.58 0.17 4.65 0.12 78.67 0.51 1.47 0.10 22.26 0.65

S1500 L50 3.49 0.10 2.12 0.07 77.28 0.64 0.64 0.04 20.27 0.77

S1500 L75 5.05 0.16 2.93 0.11 88.97 0.78 0.90 0.04 22.36 0.61

S1500 L100 3.20 0.09 1.81 0.05 90.75 1.04 0.48 0.02 20.04 0.43

S1800 L100 3.16 0.08 1.85 0.05 79.11 0.53 0.53 0.02 18.62 0.32

S2000 L100 4.01 0.14 2.46 0.10 72.32 0.44 0.67 0.03 17.84 0.31

Bio

die

sel

S1500 L25 4.89 0.16 2.42 0.23 70.61 3.66 1.78 0.24 21.85 1.15

S1500 L50 2.13 0.08 0.90 0.12 75.15 6.13 0.91 0.15 23.19 1.62

S1500 L75 2.93 0.11 1.64 0.08 67.70 0.54 0.87 0.03 21.18 0.30

S1500 L100 1.56 0.11 0.86 0.05 53.79 2.33 0.58 0.13 7.58 1.58

S1800 L100 3.03 0.28 1.31 0.07 60.83 2.41 0.93 0.13 14.86 0.01

S2000 L100 9.41 1.66 2.57 0.13 42.03 1.45 6.39 1.78 8.26 0.37

B9

6T

a4

S1500 L25 3.77 0.22 2.32 0.29 63.13 5.46 0.80 0.16 17.05 6.12

S1500 L50 1.42 0.07 0.98 0.04 59.33 1.78 0.19 0.09 11.15 2.15

S1500 L75 2.04 0.09 1.30 0.06 66.80 0.82 0.43 0.03 21.42 0.90

S1500 L100 1.71 0.12 1.06 0.06 65.86 0.77 0.34 0.06 17.55 0.61

S1800 L100 2.29 0.17 1.26 0.04 56.66 0.81 0.71 0.18 9.12 1.07

S2000 L100 8.55 1.75 2.34 0.17 41.17 1.90 5.85 1.82 8.18 0.40

B9

0T

a10

S1500 L25 3.62 0.21 1.97 0.07 53.95 0.70 1.12 0.15 11.14 1.13

S1500 L50 1.65 0.07 0.91 0.03 53.38 1.08 0.53 0.06 10.36 1.12

S1500 L75 1.91 0.07 1.18 0.06 56.00 1.29 0.44 0.03 14.28 0.59

S1500 L100 1.70 0.18 0.38 0.02 48.05 1.46 1.33 0.19 7.31 0.24

S1800 L100 3.03 0.31 1.22 0.05 49.18 1.51 1.69 0.43 8.02 0.77

S2000 L100 11.32 1.25 3.31 0.26 30.49 1.55 7.75 1.57 8.55 0.13



127

Table A.5: Particle chemical properties


Total organics [g/kWh]

× 10-7

Nitrates [g/kWh]

× 10-8

f44 Ratio of oxygenated

organic

marker to total organics

f57 Ratio of hydro-carbon

organic

marker to total organics

M1 SD2 M SD M SD M SD

Die

sel

S1500 L253 6.84 0.34 1.03 0.29 0.053 0.005 0.100 0.027

S1500 L50 6.19 0.85 1.00 0.22 0.058 0.002 0.078 0.008

S1500 L75 12.97 1.48 2.34 0.47 0.066 0.003 0.045 0.013

S1500 L100 4.74 0.22 0.63 0.07 0.059 0.011 0.048 0.002

S1800 L100 3.29 0.38 0.36 0.08 0.046 0.005 0.051 0.001

S2000 L100 3.36 0.31 0.34 0.05 0.046 0.006 0.050 0.002

B2

0

S1500 L25 3.85 0.36 0.35 0.05 0.034 0.007 0.053 0.002

S1500 L50 2.98 0.60 0.25 0.13 0.025 0.014 0.049 0.003

S1500 L75 8.31 0.24 1.08 0.04 0.024 0.002 0.050 0.001

S1500 L100 5.43 0.22 0.61 0.02 0.030 0.004 0.049 0.002

S1800 L100 3.55 0.26 0.30 0.02 0.034 0.002 0.052 0.002

S2000 L100 3.89 0.14 0.27 0.03 0.031 0.003 0.053 0.001

B5

0

S1500 L25 1.67 0.45 0.15 0.12 0.074 0.012 0.036 0.002

S1500 L50 0.99 0.11 0.14 0.06 0.059 0.007 0.042 0.002

S1500 L75 0.25 0.25 0.08 0.08 0.020 0.020 0.004 0.004

S1500 L100 2.14 0.28 0.23 0.02 0.073 0.011 0.038 0.002

S1800 L100 1.26 0.04 0.12 0.02 0.044 0.003 0.047 0.000

S2000 L100 1.38 0.17 0.13 0.04 0.048 0.005 0.047 0.002

Bio

die

sel

S1500 L25 0.76 0.17 0.24 0.06 0.056 0.031 0.045 0.006

S1500 L50 0.31 0.12 0.11 0.05 0.086 0.111 0.045 0.018

S1500 L75 0.39 0.04 0.12 0.03 0.057 0.018 0.041 0.004

S1500 L100 0.37 0.03 0.06 0.01 0.065 0.017 0.048 0.003

S1800 L100 0.53 0.10 0.07 0.02 0.054 0.006 0.053 0.003

S2000 L100 0.74 0.10 0.09 0.04 0.056 0.039 0.055 0.011

B9

6T

a4

S1500 L25 0.75 0.21 0.21 0.09 0.031 0.018 0.054 0.007

S1500 L50 0.41 0.08 0.10 0.04 0.093 0.036 0.043 0.006

S1500 L75 0.71 0.62 0.15 0.13 0.083 0.019 0.039 0.006

S1500 L100 0.70 0.60 0.10 0.13 0.081 0.020 0.038 0.007

S1800 L100 0.44 0.03 0.08 0.01 0.061 0.016 0.047 0.004

S2000 L100 0.72 0.09 0.07 0.02 0.037 0.004 0.063 0.003

B9

0T

a10

S1500 L25 1.10 0.60 0.27 0.18 0.112 0.038 0.042 0.007

S1500 L50 1.17 0.95 0.36 0.27 0.117 0.025 0.034 0.003

S1500 L75 0.70 0.16 0.15 0.04 0.168 0.010 0.026 0.003

S1500 L100 0.53 0.30 0.13 0.05 0.120 0.015 0.036 0.006

S1800 L100 0.51 0.20 0.11 0.07 0.101 0.014 0.039 0.004

S2000 L100 0.64 0.06 0.09 0.02 0.067 0.005 0.055 0.004




Table A.6: Particle physical properties


Primary particle

diameter [nm]

Fractal dimension

Radius of gyration

[nm]

Fringe Length [nm]

Fringe tortuosity

Fringe distance [nm]

M1 SE2 M SE M SE M SE M SE M SE

Die

sel

S1500 L253 15.77 0.13 1.76 0.11 45.05 2.32 0.96 0.01 1.189 0.013 0.413 0.001

S1500 L50 16.75 0.12 1.74 0.10 55.40 2.60 0.97 0.02 1.171 0.006 0.403 0.002

S1500 L75 18.61 0.15 1.78 0.10 72.51 4.44 0.99 0.02 1.166 0.008 0.399 0.001

S1500 L100 20.28 0.22 1.69 0.15 51.75 2.34 1.03 0.02 1.155 0.006 0.384 0.001

S1800 L100 16.58 0.15 1.74 0.12 44.44 1.85 0.98 0.01 1.177 0.006 0.422 0.003

S2000 L100 14.26 0.08 1.69 0.05 59.35 3.69 0.95 0.02 1.181 0.013 0.445 0.002

B2

0

S1500 L25 15.23 0.17 1.78 0.11 42.04 2.37 0.90 0.01 1.195 0.006 0.442 0.001

S1500 L50 16.27 0.14 1.75 0.10 54.04 3.60 0.92 0.02 1.180 0.006 0.438 0.002

S1500 L75 18.15 0.12 1.81 0.07 88.60 9.52 0.97 0.02 1.177 0.006 0.429 0.007

S1500 L100 19.36 0.15 1.70 0.11 48.82 2.43 0.98 0.02 1.161 0.005 0.427 0.002

S1800 L100 15.85 0.13 1.75 0.05 42.85 2.59 0.96 0.01 1.174 0.006 0.437 0.001

S2000 L100 13.96 0.08 1.72 0.06 51.21 3.65 0.91 0.01 1.187 0.005 0.444 0.001

B5

0

S1500 L25 14.64 0.12 1.82 0.09 39.77 1.86 0.80 0.01 1.218 0.006 0.462 0.001

S1500 L50 15.92 0.18 1.81 0.10 42.83 2.18 0.88 0.01 1.182 0.003 0.450 0.001

S1500 L75 17.19 0.18 1.84 0.11 64.30 6.08 0.93 0.01 1.175 0.008 0.437 0.002

S1500 L100 17.73 0.12 1.75 0.08 46.62 1.89 0.95 0.02 1.172 0.006 0.428 0.001

S1800 L100 15.4 0.1 1.76 0.06 47.29 2.15 0.93 0.01 1.195 0.007 0.439 0.002

S2000 L100 13.85 0.09 1.74 0.07 44.85 2.49 0.90 0.01 1.204 0.006 0.454 0.001

Bio

die

sel

S1500 L25 14.14 0.15 1.85 0.09 32.15 1.54 0.78 0.02 1.255 0.012 0.466 0.002

S1500 L50 15.5 0.28 1.82 0.08 28.32 1.70 0.81 0.01 1.238 0.003 0.457 0.001

S1500 L75 16.58 0.22 1.86 0.09 27.80 3.35 0.87 0.02 1.211 0.009 0.438 0.001

S1500 L100 17.26 0.21 1.75 0.07 38.07 1.89 0.93 0.01 1.199 0.007 0.428 0.002

S1800 L100 14.71 0.14 1.78 0.06 30.36 1.68 0.90 0.01 1.216 0.008 0.454 0.001

S2000 L100 13.46 0.1 1.76 0.05 37.16 1.45 0.87 0.01 1.222 0.007 0.457 0.001

B9

6T

a4

S1500 L25 13.6 0.16 1.88 0.12 31.15 1.32 0.79 0.01 1.204 0.004 0.473 0.001

S1500 L50 14.68 0.22 1.84 0.12 26.50 2.03 0.82 0.01 1.186 0.004 0.457 0.001

S1500 L75 16.04 0.24 1.88 0.15 26.75 0.83 0.89 0.01 1.180 0.006 0.441 0.003

S1500 L100 16.45 0.17 1.78 0.11 31.95 1.34 0.96 0.01 1.171 0.007 0.430 0.002

S1800 L100 14.39 0.13 1.81 0.07 35.51 2.17 0.91 0.01 1.172 0.005 0.463 0.001

S2000 L100 13.67 0.11 1.78 0.06 35.04 1.38 0.85 0.01 1.176 0.005 0.466 0.001

B9

0T

a10

S1500 L25 12.59 0.25 1.91 0.08 26.56 1.48 0.86 0.01 1.182 0.003 0.457 0.001

S1500 L50 14.02 0.25 1.87 0.07 25.18 1.21 0.87 0.02 1.174 0.004 0.451 0.002

S1500 L75 15.7 0.24 1.92 0.08 39.95 2.19 0.91 0.01 1.162 0.003 0.428 0.001

S1500 L100 15.95 0.2 1.81 0.12 30.30 1.59 0.99 0.02 1.158 0.005 0.420 0.001

S1800 L100 14.09 0.13 1.85 0.08 34.66 1.40 0.98 0.01 1.162 0.005 0.431 0.001

S2000 L100 13.31 0.11 1.79 0.08 32.29 1.34 0.93 0.01 1.172 0.005 0.453 0.001

1M = Mean value 2SE = Standard error 3S = Speed, L = Load


129

Appendix B

Further information on knock experiment

Further information on the experimental setup and data:

Engine:

• Single cylinder SI engine by Daimler AG (based on M270 DE 16)

• Direct injection at central injector position

• Downsized and turbocharged

• Exhaust gas throttle

• Lambda = 1

• Compression ratio: 10.3:1

• Injection pressure: 200 bar

• Intake air: externally charged, temperature conditioned between 25 and 30 °C,relative air humidity kept at around 10%

• Maximum pressure: 110 bar

• Oil temperature: 90 ±2°C

• Pressure transducer - nearly flush-mounted piezoelectric cylinder pressure sen-sor Kistler 6061BU20 (eigenfrequency of 90 kHz)

• Pressure indication system DEWE-800 by Dewetron, resolution of 0.05°CA

• Fuel: petrol with ethanol 5% ethanol (E5), petrol with 25% ethanol (E25), andneat ethanol (E100)


130 Appendix B. Further information on knock experiment

Knocking provoked by different operating strategies:

• Increased boost pressure at constant centre of gravity

• Constant boost and exhaust back pressure at increasingly early ignition timings

• Different engine speeds

• Different injection timings


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