Applied Acoustics 88 (2015) 66–74

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Applied Acoustics

journal homepage: www.elsevier .com/locate /apacoust

A novel robust automated FFT-based segmentation and featuresselection algorithm for acoustic emission condition based monitoringsystems

http://dx.doi.org/10.1016/j.apacoust.2014.08.0070003-682X/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author at: Department of Mechanical and Industrial Engineering,College of Engineering, Qatar University, P.O.Box 2713, Doha, Qatar. Tel.: +974 44034319.

E-mail address: samer@qu.edu.qa (S. Gowid).

Samer Gowid a,b,⇑, Roger Dixon a, Saud Ghani b

a School of Electronic, Electrical and Systems Engineering, Faculty of Engineering, Loughborough University, P.O. Box LE11 3TU, Leicestershire, UKb Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, P.O. Box 2713, Doha, Qatar

a r t i c l e i n f o a b s t r a c t

Article history:Received 10 January 2014Received in revised form 11 August 2014Accepted 12 August 2014

Keywords:Condition based monitoringSegmentation algorithmFeatures selectionCentrifugal equipment and fault detection

This paper aims at developing a robust, fast-response and automated FFT-based features selection algo-rithm for the development of acoustic emission practical condition based monitoring applications ofmechanical systems. Further scope of this work is to investigate the suitability of acoustic emission forthe fault diagnostic of high speed centrifugal equipment using a single AE sensor.

Experiments were conducted using an industrial air blower system with a rotational speed of15,650 RPM. Five experiments for five different machine conditions were carried out. Ten data sets werecollected for each machine condition with a total number of 50 data sets. Fifty percent of the data setswere used for training and the remaining data sets were used for verification. Tailor made programsfor spectral features selection and for classification of faults were developed using Maltab to implementthe proposed algorithm to an industrial air blower system. The results showed the suitability of theacoustic emission spectral features technique for the fault diagnostic of centrifugal equipment andproved the effectiveness and competitiveness of the proposed automated features selection algorithm.The sets of features selected by the algorithm yielded a detection accuracy of 100%.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Condition based monitoring (CBM) of machinery significantlyreduces the cost of maintenance and allows an early detection ofpotential catastrophic faults. These catastrophic faults usually leadto detrimental consequences and are extremely expensive torepair. Unlike preventative and corrective maintenance strategies,the implementation of CBM sharply lowers the maintenance costby preventing major failures and by delaying scheduled mainte-nances until convenient or necessary.

Centrifugal equipment such as blowers, gas turbines and com-pressors are of high importance as they are widely used in manyindustries, in particular the oil and gas industry. The reliability ofthese centrifugal equipment can be either improved by introducinga standby system or by increasing the system availability. Effectivefault diagnostic tools decrease planned shutdowns and henceincrease the availability of systems [1]. Bearings are essential

components of any centrifugal equipment. Bearing faults occurdue to fatigue under unbalanced operation, improper lubrication,contamination or installation errors. Currently, major bearingfaults detection systems are either based on vibration or AcousticEmission (AE) signatures as the vibration and noise levels associ-ated with bearing deterioration increase. Major vibration and AEsignals’ features are Root Mean Square (RMS)., crest factor, energy,counts and peaks, amplitude and Fast Fourier Transform (FFT) [2].The FFT features give useful information for rotating componentssince well-defined frequency components are associated withthem [3]. The FFT-based features selection process is a key rolein FFT-based condition based monitoring systems as it directlyaffects the efficiency of diagnostic process. Hence, an effectiveautomated features selection approach is required to improve theefficacy and to automate FFT-based fault detection systems.

The AE proved its effectiveness in detecting machine faults.Unlike the vibration technique, the AE is less affected by noise andby structural vibration. It is difficult to utilize the fault bearing vibra-tion spectral features to detect a bearing fault of a bearing installedin a mechanical system as the resonance frequencies of the struc-tures between the bearings and the transducers are excited to makemost of bearing fault signatures exist at high-frequency resonant

S. Gowid et al. / Applied Acoustics 88 (2015) 66–74 67

bands [4]. AE based condition monitoring techniques for rolling ele-ment bearings and gears are widely used as a basis for preventivemaintenance such as Shock Pulse Method (SPM). SPM is an AE basedsignal processing technique used to measure rolling noise and metalimpact caused by the compression between roller elements bearingand raceways. SPM detects ultrasonic signal within a narrow fre-quency band near 32 kHz and the intensity of the ultrasonic signalincreases as the damage increases [5,6].

Recently, a significant research effort was observed toward thedevelopment and implementation of efficient and automatedmachinery fault detection and diagnostic tools. The majority ofexisting tools are based on either various conventional pattern-rec-ognition schemes or artificial intelligence techniques such as Arti-ficial Neural Networks (ANN) and Fuzzy Logic (FL) systems.Although the computing time and computational cost of conven-tional non artificial intelligence approaches are low, the majorityof the current features selection tools are based on artificial intel-ligence techniques which usually require high computational costand long computing time.

In this paper, a fast robust and automated FFT-based featuresselection algorithm is developed. Suitability of utilization of AEspectral features for the condition based monitoring of centrifugalequipment is also assessed using an industrial centrifugal blowersystem. Since it is not practical and very difficult to manually selectthe features for a large number of data sets and due to the longcomputing time and high computation cost of artificial intelligencetechniques, this work presents a new features selection algorithmwith a fast response and low computation. Unlike existing spectralfeatures selection algorithms which are based either on visualinspection or on artificial intelligence approaches, the proposedalgorithm utilizes an effective conventional systematic approachthat automates the selection process and reduces both computa-tional cost and computing time.

The principal contributions of the present paper are the intro-duction of a low cost fast conventional non-artificial intelligencebased spectral features selection algorithm and the suitabilityassessment of AE spectral features for condition based monitoringof high speed centrifugal equipment using a single AE sensor.

This paper has five main sections. Section 2 summarizes theprevious related research work. Section 3 illustrates the experi-mental setup. Section 4 details the experimental work. Section 5describes the developed features selection algorithm while Section6 discusses the results of the proposed algorithm and of the suit-ability of utilizing AE for condition based monitoring of centrifugalequipment.

2. Literature survey

Currently, the majority of fault diagnostic systems are based ontwo techniques. The first technique is based on the traditional timeand frequency analyses while the second technique is based onartificial intelligence which takes neural network method as a rep-resentative. The traditional techniques have a reasonable perfor-mance in detecting faults but need a prior knowledge in additionto a numerous fault samples. The artificial intelligence techniquesalso have a reasonable performance but need a high computationalcost and a long computing time. The following related researchwork illustrates the different techniques used for signals’ featuresselection.

Saxena and Saad [3] proposed the utilization of Genetic Algo-rithm (GA) with ANN for identifying near optimal feature set forANN fault diagnostic systems. Nine bearing health conditions wereemulated; eight bearings with different crack sizes in addition to ahealthy bearing. The cracks were constructed using an Electric Dis-charge Machine (EDM). Three accelerometers and one acoustic

emission sensor were utilized. Five features options were set asinputs for the GA namely statistical features, statistical on sumand difference signals, spectral features and all features together.The FFT analysis was based on 32 values for each signal. The resultsshowed that the technique of using GA for selecting an optimal fea-ture set for a classification application of ANN is powerful and thatthe collective use of all features was best. The GA optimized thebest combination based on the performance obtained directly fromthe success of classifier and the mean classification success was99.94%.

The algorithm developed by Saxena and Saad included the spec-tral analysis but it did not change the number of segments to betterdistinguish the fault. The number of segments should be automat-ically changed based on the number of faults and based on the dif-ference between values in order to optimize the accuracy of thedetection process. Moreover, the algorithm was not tested for thedetection of simultaneous faults. The authors did not investigatethe effectiveness of the proposed algorithm in selecting the featureset for multiple-fault classifier and in solving the problem of faultinterference.

The Support Vector Machine (SVM) is an artificial intelligencemethod based on the principle of statistical learning theory andwas utilized for both feature selection and classification processes.The SVM method started to be utilized for both feature selectionand classification [7]. Meng et al. [8] presented a new conditionmonitoring and analysis method for small samples studies suchas reactor coolant pump based on SVM. The data were passedthrough a multi-band FIR filter to eliminate the noise and uselessfrequency. Kernel principal component analysis was utilized todecrease the dimension of the vector, processing time and accu-racy. This method is used as both two-kind classifier and multiple-classifier and could separate the different running conditionssuccessfully.

The approach developed by Meng et al. was not validated forthe detection of simultaneous faults (unbalance and friction faults).The authors did not investigate the effectiveness of the proposedtechnique for distinguishing multiple-fault.

Gryllias et al. [9] utilized the SVM for the selection of optimalfeatures due to the lack of actual experimental data. The input fea-tures were divided into two groups: (a)Traditional signal statisticalfeatures in the time domain, such as mean value, RMS, VAR, SK,kurtosis, (b) Frequency domain based indices, such as energy val-ues obtained at characteristic frequency bands of the measuredand the demodulated signals. The main advantage of this workwas that the training of the SVM was based on a model describingthe dynamic behavior of a defective rolling element bearing,enabling the direct application of the SVM to experimental mea-surements of defective bearings, without the need of training theSVM with experimental data sets of a defective bearing.

Gryllias et al. did not consider the fault interference problem andonly studied the occurrence of a single fault. Finding the dynamicequation for each component in a complex system was difficultand time consuming. To implement this approach, the CBM systemdeveloper will still need to verify the results experimentally and theapproach will not help minimize the developing cost and time.

Samhouri et al. [10] proposed a new approach based on thecombination of the axial vibration time signal features of carnallitesurge tank pump namely RMS, variance, skewness, kurtosis, andnormalized sixth central moment. These features were utilized asinputs to both Adaptive Neuro Fuzzy Inference System (ANFIS) andANN. Three different faults with three different fault codes wereemulated. A total of 92 runs were conducted; 73 runs for trainingand 19 runs for testing. The comparison showed that the adoptionof the time root mean square and variance features achievedthe minimum fault prediction errors for both ANFIS and ANN.The trapezoidal membership function in ANFIS achieved a fault

Impeller

Blower case

Fig. 1. Single stage centrifugal blower.

68 S. Gowid et al. / Applied Acoustics 88 (2015) 66–74

prediction accuracy of 95%, while the cascade forward back-propagation ANN achieved a better fault prediction accuracy of 99%.

Samhouri et al. proved the effectiveness of the ANN techniqueover the ANFIS technique. The authors did not mention the typesof the emulated faults and did not utilize the spectral analysis tech-nique as one of the major vibration analysis techniques. As no mul-tiple-faults simulations were carried out, the effectiveness of theproposed approach for distinguishing machine multiple-fault isvague.

Gupta and Wadhwani [11] proposed a robust Genetic Program-ming (GP)-based feature selector for the selection of best featuresfrom large features data set for bearing fault classification. ANN clas-sifier was utilized for the recognition of fault patterns. Vibrationtime domain features were extracted from the statistical measuresof Median, RMS, Crest Factor, histogram Lower Bound (LB), histo-gram Upper Bound (UB), Entropy (ENT), Skewness (SK), Kurtosis(KT), Variance (VAR), Shape Factor (SHF), Impulse Factor (IMF),and Clearance Factor (CLF). Experimental data were collected forfour bearings conditions namely healthy, defective Outer race,defective Inner race and defective ball fault condition. The algorithmwas utilized to effectively select a smaller subset of features. All theeight features were selected by GP and yielded a detection accuracyof 99.99%.

Recently, several research work was conducted on bearing faultdiagnostics using wavelet and envelope analysis techniques. Theresearch was focused on the analysis of signals to improve the pat-tern detection. Unlike artificial intelligence techniques, automatedselection of best features off large features data was not investi-gated. The studies did not develop features selectors that identifyfeatures with larger inter differences.

Lin and Qu [12] proposed the wavelet entropy as a new signalanalysis technique. The authors used the wavelet analysis and var-ied the shape factor of the Morlet wavelet to achieve the minimumwavelet entropy for bearing fault feature selection. Qiu et al. [13]used the Shannon entropy and singular value decomposition tooptimize the wavelet entropy and kurtosis parameters. Bozchalooiand Liang [14] introduced the smoothness index to guide theparameter selection of the complex Morlet wavelet for de-nosingbearing fault signal.

Wang et al. [15] observed that if a bearing suffers a localizedfault, the transients with a potential cyclic characteristic are gener-ated by the rollers striking the localized fault. This phenomenon isconsidered as an early bearing fault feature. Therefore, the extrac-tion of the transients is beneficial to the identification of earlybearing fault. The authors proposed a novel Adaptive WaveletStripping Algorithm (AWSA) to extract the simulated transientsfrom an original bearing fault signal. The results showed that theAWSA can adaptively peel the simulated transients from the origi-nal bearing fault signals. A comparison between AWSA and peri-odic multi-transient model was conducted to show that theproposed approach is better in selecting the random characteristicsof the real transients. An enhanced AWSA was also developed toreduce the computing time.

Shen et al. [16] proposed an automated sensory feature selec-tion method to reduce the developing time and cost of conditionbased monitoring systems for machining operations. Force, accel-eration, sound and acoustic emission sensors were utilized forthe detection of high-speed milling operations. Time domain, fre-quency domain and wavelet analysis techniques were employedto analyze the measured signals. Gradual tool wear was used forevaluating the proposed self-learning automated sensory featureselection method. The results showed that the proposed methodcan be applied through an automated and self-learning monitoringprocess for the selection of the most suitable sensors.

Bechhoefer et al. [17] proposed a bearing envelope basedwindow selection technique to improve the fault selection. The

envelope analysis is a well-known signal processing techniquefor bearing fault detection. The performance of Spectral Kurtosis(SK) and Envelope Kurtosis (EK), as a technique for setting anoptimal frequency and bandwidth window for the envelopeanalysis, was quantified.

Patel et al. [18] utilized the envelope analysis and Duffing oscil-lator to detect local defects existing on races of deep groove ballbearings in the presence of external vibration. The study confirmedthat the defect detection mainly depends on the selection of centerfrequency and bandwidth. The spectra of selected center frequencywith several bandwidths were compared for identification ofdefective frequency. The authors concluded that the utilization ofDuffing oscillator improved the detection performance.

Based on the above research work, it is evident that the com-puting time and computational cost of artificial intelligence basedfeatures selector are high. Various techniques such as GA and SVMwere utilized to decrease both computing time and computationalcost. Due to the difficult implementation of the above techniques,the developing time and cost of the current CBM systems are high.A research gap in developing FFT-based features selection algo-rithms using a conventional non-artificial intelligence approachwas observed.

3. Experimental setup

Experimental tests were conducted in a laboratory environmenthosted by Qatar University using a Paxton AT1200 industrial sin-gle-stage centrifugal air blower system. The blower has a flow rateof 1000 m3/hr and a pressure of 1.24 BarA. Fig. 1 shows the singlestage centrifugal compressor.

The air blower system consists of a 15 HP DC motor, DC inverterfor motor speed control and a centrifugal air blower. Four factorycalibrated AE sensors from Physical Acoustics were utilized tomeasure the AE signals; two low frequency range sensors withan operating range of 35–100 kHz (Model: R6a) and two high fre-quency range sensors with an operating range of 100–1000 kHz(model: UT1000). The AE sensors were positioned as close as pos-sible to the bearings as shown in Figs. 2 and 3. However, AE sensorcan measure any frequency outside its operating bandwidth butwith less sensitivity.

The schematic of the experimental setup is shown in Fig. 3. TheAE sensors were attached to signal conditioners and programmablelow pass filters with isolated grounds to combat the problem of ali-asing in sampling signals. A cut off frequency of 200 kHz was set toattenuate high frequency AE signals. The models of bearings (A)and (B) are DKT-7203BMP and FAG-2203TV, respectively.

The data was collected using an MSeries- PCI 6250 NationalInstruments data acquisition board with 16 channels, 16-bit reso-lution and 1.25 MS/s sampling rate.

R6a AE sensor

UT1000 AE sensor

Bearing case

Fig. 2. Positions of AE sensors.

Fig. 3. The schematic of the experimental setup.

Table 1Machine health conditions.

Machine condition Bearing (A) Bearing (B) Leakage

Condition 1 (MC 1) Healthy Healthy NoCondition 2 (MC 2) Healthy Healthy YesCondition 3 (MC 3) Outer race defect Healthy NoCondition 4 (MC 4) Healthy Outer race defect NoCondition 5 (MC 5) Outer race defect Outer race defect No

S. Gowid et al. / Applied Acoustics 88 (2015) 66–74 69

4. Design of experiment

Bearing problems account for over 40% of machine breakdowns[8]. Thus, this experimental work focuses on bearings faults in Cen-trifugal blowers and will investigate the problem of fault interfer-ence as well.

Fig. 4 illustrates the faults of bearings (A) and (B). Bearing (A)has a 3 mm throughout hole in the outer race while bearing (B)has four notches in both sides with a maximum groove width of2 mm.

Five machine conditions were emulated as shown in Table 1.Several tests were conducted under three different operationalspeeds to check the functionality and proper installation of sensorsusing the experimental setup shown in Fig. 3. The speed wasincreased from 3600 to 6960 RPM, and then to 15,650 RPM. TheR6a sensor which is directly positioned above bearing (A) gavethe highest reading at 15,650 RPM. Hence, as the experiment was

(a) Bearing (A)

Fig. 4. Notches in the outer ra

designed to use a single AE sensor, bearing (A) R6a sensor wasselected for its proper installation and high sensitivity.

Five experiments were conducted at a rotational speed of15,650 RPM. The first experiment emulated the healthy conditionwhile the other four experiments emulated four different fault con-ditions. The data were sampled using the high speed NI DAQ boardat a sampling rate of 1 MS/s for a time period of 187 s. For each ofthe five conditions, 10 data sets were collected at a fixed timeinterval of 13 s (one set every 13 s). Each data set has a size of1 � 106 samples at 1 MHz. The first data sets for the five machineconditions were recorded 60 s after the blower reached its fullrotation speed. Fifty percent of the 50 data sets were used for train-ing (DS# 1, 3, 8, 10 and 5) while the rest were used for verification.

5. Features selection algorithm

Segmentation process breaks the main frequency domain rangeinto smaller groups of frequencies. The segment size depends onthe value of the features difference required to clearly differentiatebetween different machine conditions. Failure in differentiatingbetween machine conditions at the smallest segment size meansthat the utilized technique is not suitable for the detection system.Hence, the proposed algorithm can also be utilized to evaluate dif-ferent FFT-based fault detection techniques. The computing timerequired for building, analyzing, verifying and testing the segmentswill increase with every additional segment the program creates.The segment size (S) range which was selected for this applicationis from 1000 to 119,000 Hz with a loop step of 1000 Hz.

The main focus of the proposed algorithm is to automaticallyselect the major spectral features to better recognize the fault pat-tern. This is carried out by converting the signal from time domainto frequency domain and then by segmenting the Frequency spec-trum into equal groups of frequencies. The program calculates themax amplitude value in each segment and then compares all max-imum amplitudes to the pre-determined benchmark thresholds tocheck whether the machine condition pattern is recognized. Theprogram starts by reading twenty training Data Sets (DS); four datasets (DS# 1, 3, 8 and 10) for each machine condition for a totalnumber of five machine conditions. The flowchart shown inFig. 5 illustrates the different processes.

(b) Bearing (B)

ces of bearings (A) & (B).

Y

Y

Start

Load the 20 training data

sets

S=119,000: -1000:1000

KHZ

END

FFT Segments matrix

Process D

F.Pre-Identification matrix

Process E

F.Identification matrix

Five F Selection matrices

Five F Selection Matrices

Process C

Process A

Div=2000:S:121,000

XBM matrix (Xmax & Xmin)

Process B

Five P matrices

Five new segmented data sets

S: Segment size in KHzK: Segment number

FFT

Fig. 5. Flowchart of the proposed algorithm.

Table 2(a) XBM – Xmax section. (b) XBM – Xmin section.

Segment 1 (S1) (2: < 109 kHz) Segment 2 (S2) (109: 121 kHz)

(a)MC 1 19795 492MC 2 14934 457MC 3 55989 270MC 4 85780 15621MC 5 36788 6008

(b)MC 1 13312 446MC 2 9422 451MC 3 45789 163MC 4 79350 9871MC 5 23106 4749

70 S. Gowid et al. / Applied Acoustics 88 (2015) 66–74

Process A (First training cycle – XBM matrix): this process takesthe following inputs: the number of machine health conditions(NoC), the segment size (S) and calculates the segment number(K) based on the number of range (Div). It also calculates the max-imum segments’ FFT amplitudes of training data sets (DS# 1, 3, 8and 10), outputs the maximum and minimum amplitude valuesfor each machine condition, and finally puts them in Xmax(NoC,K) and Xmin(NoC, K) matrices. These two matrices are combinedand considered as a benchmark thresholds matrix for all machineconditions. Table 2 shows the output of Process A at K = S1: S2,Kn = 2, and NoC = 1:5. Column 1 shows the machine conditionnumber while columns 2 and 3 illustrate the maximum and mini-mum values of FFT amplitudes in segment 1 and 2, respectively.

Process B (Second training cycle – P matrix): the main focus ofthe second cycle is to make the program more adaptive and more

Table 4(a) F Pre-Identification matrix at a segment size of 109 kHz. (b) F Pre-Identificationmatrix at a segment size of 1 kHz.

X1 X2 X3 X4 X5

(a)MC 1 2 1 0 0 0MC 2 2 3 0 0 0MC 3 0 0 2 0 0MC 4 0 0 0 4 0MC 5 0 0 1 0 4

(b)MC 1 182 65 13 4 36MC 2 63 157 17 2 20MC 3 18 21 172 2 22MC 4 3 2 2 203 3MC 5 37 33 29 0 167

S. Gowid et al. / Applied Acoustics 88 (2015) 66–74 71

flexible to changes over time as the algorithm uses the new datasets to identify the segment sizes at which all machine conditionpatterns are recognized. Hence, the benchmark thresholds’ ampli-tudes are calculated once and the program can then adapt thechanges through a second training cycle using a single data set.This part of algorithm takes the XBM matrix in addition to fivenew AE data sets (DS# 5) as inputs; one data set for each machinecondition, segments the new signals into Kn segments, calculatesdifference percentages between the amplitudes of the correspond-ing new segmented data set and the benchmark thresholds’ ampli-tudes at each and every segment (K), and yields five percentagematrices (P1–P5) that include all difference percentages of all seg-ments for all new data sets.

For example, a new data set for machine condition 2 is con-verted to frequency domain and then segmented into Kn segments.The maximum amplitude of each segment is calculated and thencompared to all corresponding benchmark thresholds’ amplitudes(XBM). The results of this comparison are put into P2 matrix.

Process C (Features selection – F Selection matrix): This processbuilds a matrix portioned into two equal sections called FSelection.For the first section, the program compares the maximum ampli-tudes of each of the five new AE data sets to all XBM elements atsegments (K) yielding five FSelection matrices. Then, if the ampli-tude of the new data set doesn’t fit inside the range X (FromXmax(NoC, K) to Xmin(NoC, K)), the program sets the correspondingFSelection element value to zero. Otherwise, the value is set basedon the machine condition number which has the amplitude valuewithin its amplitude interval. The machine condition numbersare 1, 2, 3, 4 and 5. The second section of the FSelection matrix iscalculated by selecting the minimum absolute percentage of thecorresponding P matrix (P1–P5). The corresponding FSelectionmatrix elements are set to 1, 2, 3, 4 or 5 based on the machine con-dition number (NoC). The minimum absolute percentage isselected to limit the selection to only one pattern (one machinecondition) for each data set of the five new data sets. A zero valueis set when two or more identical P matrix elements are observed.A zero value means that this segment cannot be utilized for detect-ing this fault. Table 3 shows a calculated FSelection matrix formachine condition number 5 at a segment size of 109 kHz. X5 dataset was compared to XBM to obtain the matrix shown in Table 3.Column 2 shows the machine condition number while columns 3and 4 illustrate the machine condition number detected by thealgorithm in segments 1 and 2, respectively.

Process D (Fault pre-identification – F Pre-Identificationmatrix): This process merges the five FSelection matrices intoone matrix called Pre-decision by counting the features that matchwith the benchmark threshold of each machine condition. Thematrix consists of five columns; one column for each new AE dataset, and five rows; one for each machine condition. Table 4 illus-trates the outputs of process D at different segment sizes. Column1 shows the machine condition number while the other columns

Table 3The FSelction5 matrix used for the detection of machine condition 5 at a segment sizeof 109 kHz.

Segment 1 (S1) Segment 2 (S2)

Section I MC 1 0 0MC 2 0 0MC 3 0 0MC 4 0 0MC 5 5 5

Section II MC 1 0 0MC 2 0 0MC 3 0 0MC 4 0 0MC 5 5 5

illustrate the sum of signal features matched with each machinecondition for each DS#5 signal X1, 2, 3, 4 and 5.

Process E (Fault identification – F Identification): This part ofthe algorithm checks whether the different machine conditions’patterns are recognized. The calculation is based on the Pre-Identi-fication matrix where the program selects the machine conditionwith the largest features number. If the selection is correct and ifthe machine condition is successfully detected at this set of seg-ments, the program sets the value of the corresponding F-Identifi-cation matrix element to (1), while other column element valuesremain zeros. Otherwise, the program does not change the valueof the corresponding F-Identification matrix element and willremain as zero column elements. Table 5 shows the output of Pro-cess E at two different segment sizes. Column 1 shows the machinecondition number while the other columns illustrate the detect-ability score for each DS#5 signal X1, 2, 3, 4 and 5.

Fig. 6 illustrates the outputs of processes A, B, C, D and E at asegment size of 109 kHz. In this figure, only one of the five newdata sets was processed and the other four data sets should be pro-cessed in order to have the Pre-Identification and Identificationmatrices completed. Process A outputs the benchmark matrix(XBM) that includes the maximum and minimum benchmarkamplitudes at each segment for all machine conditions. XBM andX1 matrices are used calculate the P1 matrix in process B. The pro-cess should finally output five P matrices for five new data sets (X1,X2, X3, X4 and X5). Process C compares X1 matrix to XBM matrix tocalculate the first section of F Selection matrix. The second sectionof FSelection matrix is calculated by selecting the minimum per-centage at each segment. In Section II of FSection matrix, number1 in segment S1 means that the first segment can detect MC1 andnumber 2 in segment S2 means that the second segment can detectMC2. Process C should finally output five FSelection matrices; one

Table 5(a) Fault identification matrix at a segment size of 109 kHz. (b) Fault identificationmatrix at a segment size of 1 kHz.

X1 X2 X3 X4 X5

(a)MC 1 0 0 0 0 0MC 2 0 1 0 0 0MC 3 0 0 1 0 0MC 4 0 0 0 1 0MC 5 0 0 0 0 1

(b)MC 1 1 0 0 0 0MC 2 0 1 0 0 0MC 3 0 0 1 0 0MC 4 0 0 0 1 0MC 5 0 0 0 0 1

Fig. 6. Example on how the algorithm processes a new data set of MC1 at a segment size of 109 kHz. K ranges from S1 to S2 (two segments).

Table 6FFT segments matrix for different machine conditions at a descending segment sizesranging from 119 to 1 kHz.

S (kHz) MC 1 MC 2 MC 3 MC 4 MC 5

119 0 1 0 1 0118 0 0 1 1 1117 1 1 0 1 1116 1 1 0 1 1115 0 1 0 1 1114 0 1 1 1 1113 0 1 1 1 1112 0 1 1 1 1111 0 1 1 1 1110 0 1 1 1 1109 0 1 1 1 1108 1 1 1 1 1107: 2 1 1 1 1 11 1 1 1 1 1

72 S. Gowid et al. / Applied Acoustics 88 (2015) 66–74

matrix for each machine condition using X1, X2, X3, X4 and X5. Pro-cess D calculates five Fault Pre_Identification matrices based on thepre-calculated FSelection matrices. In this process, the programsums the features of each machine condition. For example, twofeatures for MC1, two features for MC2 and no features for others.Process E selects the machine condition with the largest numberof features and puts it into the Fault Identification matrix. Finally,the Fault Identification matrix will be transformed into a columnmatrix and will be inserted into its corresponding FFT Segmentsmatrix column to give the detection information at this segmentsize.

The FFT segments matrix is built to show the detectability ofdifferent machine conditions at different segment sizes (S). Thesesegments sizes are passed to a fault detection algorithm for robustmachine condition detection. This matrix is calculated based onthe previously calculated Identification matrix by simplifying thematrix to a column matrix with a size of 5 � 1, and then by insert-ing the simplified matrix into the FFT Segments matrix. Hence, thefinal dimension of the matrix will be (5 � S). Table 6 illustrates anFFT segments matrix for five machine health conditions(NoC = 1:5) at all segment sizes (Sn = 119:1 kHz). The segmentsizes with all row elements equal to 1 are to be passed to the faultclassification algorithm along with their corresponding confidencelevels. The fault detection algorithm will then select the mostappropriate segment size based on the required overall confidencelevel. The confidence level is defined as the difference between thesum of features of the detected machine condition and the secondhighest sum of features of other machine condition. The overallconfidence level is the smallest confidence level number in a fiveconfidence levels array; one for each machine condition. Per exam-ple, based on Table 4(b), the calculated difference values betweenthe sums of machine condition features (X1, X2, X3, X4 and X5) at asegment size of 1 kHz are 119 (182–63), 92, 143, 199 and 131,

respectively. Hence, the smallest number of these difference valuesis 92, which is considered as the overall confidence level value. Thelarger overall confidence level numbers the better. Hence, althoughthe computing time at a segment size of 1 kHz is relatively high,the accuracy of the solution at this segment size is best. Column1 Table 6 shows the biggest segment size while the other columnsillustrate the detectability score of different machine conditions atthe corresponding segment size.

6. Discussion

The proposed segmentation and fault detection algorithm wastested using 25 different data sets. Five data sets for each healthcondition were collected to evaluate the performance of the

2 4 6 8 10 12

x 104

0

5000

10000

15000

Frequency (Hz)

Am

plitu

de

Fig. 7. AE FFT spectrum for DS#1 of machine condition (MC1).

2 4 6 8 10 12

x 104

0

1

2

3

4

5

6

7

8

9 x 104

Frequency (Hz)

Am

plitu

de

Fig. 8. AE FFT spectrum for DS#1 of machine condition 4 (MC4).

S. Gowid et al. / Applied Acoustics 88 (2015) 66–74 73

algorithm. All health conditions were successfully detected with asuccess rate of 100%.

Table 7 compares the detection performance of the proposedalgorithm with the detection performance of simple classifiersusing DS#5. The simple classifier yielded a maximum detectionaccuracy of 60% while the proposed algorithm yielded 100% detec-tion accuracy at a segment size of 108 kHz. Moreover, both the per-formance and confidence level of the proposed algorithm areadjustable and are related to the segment size while they arenon-adjustable in simple classifiers. The benchmark thresholdswere calculated based on the training data sets # 1, 3, 8 and 10,Columns 2 and 3 show the benchmark threshold ranges [Xmin,Xmax] while column 4 shows the maximum FFT amplitudes of dataset number 5. Data sets # 1, 3, 8 and 10 were utilized to calculatethe maximum and minimum threshold ranges.

Figs. 7 and 8 show two samples out of 25 training AE FFT fre-quency spectrums; the first figure for a healthy machine condition(MC 1) and the second figure for a faulty machine condition (MC 4).Fig. 7 illustrates the maximum amplitude of MC1 (DS#1) in seg-ment 1 which is equal to 14,413 while Fig. 8 illustrates The maxi-mum amplitude of MC 4 (DS#1) in segment 1 which is equal to85,780. The peak amplitude shown in Fig 8 can be identified as aShock Pulse based on SPM. Shock Pulse occurred near a center fre-quency of 32 kHz and the value of its amplitude is relative to bothseverity of damage and velocity of impact between rolling ele-ments and bearing outer raceway.

Table 8 illustrates that, at 109 kHz segment size, the healthycondition (MC1) was not detected. Hence, this data set can be fal-sely diagnosed and may affect the overall detection performance.At a segment size of 108 kHz, although all machine conditionswere successfully detected as shown in Table 6, the overall confi-dence level is small and this may negatively affect the accuracyof detection. The overall confidence level is best at 1 kHz segmentsize and this significantly improves the performance of fault classi-fication process. On the other hand, at this small segment size, thecomputational cost and computing time are relatively high.

A tradeoff is merely required to find the most suitable segmentsize at the lowest computational cost and processing time. Basedon Table 6, all machine conditions are detectable at any segmentsize smaller than 109 kHz. Table 4 shows the Pre-identificationmatrices for all machine conditions at segment sizes 1 and108 kHz. Although the accuracy and certainty increase with theincrease of inter difference value between the sums of features ofdifferent data sets, the computational cost and computing timeof the algorithm will be relatively high due to the large numberof segments. This computational cost and processing time are stillvery low in compare to the time needed to solve the same problemusing Artificial Neural Network (ANN) technique even if some opti-mization techniques are applied to ANN [3].

Table 8 summarizes the results of the verification process. Fiftypercentages of the data (Five data sets for each machine condition)were utilized for verification. The algorithm managed to detect allmachine conditions with an accuracy of 100%.The Overall

Table 7Benchmark threshold values of non-segmented frequency spectrums (Xmin and Xmax matr

Machine condition Xmin FFT amplitude Xmax FFT amplitude

MC1 1.33E + 04 1.98E + 04MC2 9.42E + 03 1.49E + 04MC3 4.58E + 04 5.60E + 04MC4 7.94E + 04 8.58E + 04MC5 2.31E + 04 3.68E + 04Performance

a The detectability score of different faults using a simple classifier.b The detectability score of different faults using the proposed algorithm at S = 108 kH

confidence level at 1 kHz is much higher than the overall confi-dence level at 108 kHz as the differences between features’ sumsat 1 and 108 kHz are large. Column 1 Table 8 shows the actualmachine condition. Column 2 shows all machine condition num-bers while other columns illustrate the sum of signal featuresmatched with each machine condition for 5 different data sets.

The signatures included in this study are collected under labo-ratory operating conditions as all faults were artificially created.Disturbances in the laboratory are little or null and the tests arecarried out at constant speed. To apply this approach to real sys-tems, the signatures should be recorded under real operating con-ditions to consider the disturbances and change in speed. Theproposed algorithm has the capability to consider disturbancesand abnormal operating conditions providing training to capturesignatures of major faults and their combination.

In this research, since all faults were successfully detected, thefalse alarm rate is null. The fault can be falsely categorized ifthe fault signature largely changed over time either due to the

ices).

DS#5 FFT amplitude Detectabilitya Detectabilityb

1.29E + 04 0 11.10E + 04 1 13.71E + 04 0 18.50E + 04 1 12.45E + 04 1 1

60% 100%

z (2 segments).

Table 8Fault pre-identification matrices for 25 data sets at two different segment sizes.

Segment size: 1 kHz 108 kHz

Data set#: 2 4 6 7 9 2 4 6 7 9

Healthy MC 1 177 182 170 182 165 3 3 3 3 2MC 2 56 63 65 63 61 1 1 1 1 0MC 3 21 18 17 18 23 0 0 0 0 0MC 4 2 3 6 3 3 0 0 0 0 0MC 5 36 37 35 37 37 0 0 0 0 1

Leak MC 1 68 65 64 65 59 1 0 0 0 0MC 2 166 157 165 157 165 2 4 4 4 3MC 3 21 21 24 21 24 0 0 0 0 0MC 4 3 2 4 2 3 0 0 0 0 0MC 5 33 33 37 33 31 0 0 0 0 0

Impeller MC 1 11 13 20 13 18 0 0 0 0 0MC 2 20 17 15 17 15 0 0 0 0 0MC 3 165 172 187 172 176 2 2 4 2 3MC 4 1 2 1 2 2 0 0 0 0 0MC 5 31 29 27 29 26 1 1 0 1 0

Belt MC 1 1 4 2 4 3 0 0 0 0 0MC 2 4 2 0 2 4 0 0 0 0 0MC 3 0 2 2 2 1 0 0 0 0 0MC 4 198 203 190 203 189 3 4 4 4 4MC 5 2 0 2 0 0 0 0 0 0 0

Both MC 1 29 36 26 36 20 0 0 0 0 0MC 2 24 20 21 20 18 0 0 0 0 0MC 3 20 22 19 22 11 0 0 0 0 0MC 4 7 3 2 3 2 0 0 0 0 0MC 5 165 167 177 167 172 3 4 4 4 3

74 S. Gowid et al. / Applied Acoustics 88 (2015) 66–74

occurrence of an unpredicted fault or due to an abnormal change inoperating conditions.

The proposed algorithm can be applied to different industrialsystems as the decision making is based on data provided by thetechnical staff. The fault signatures should be recorded under realoperating conditions. However, the detection system must be welltrained to accommodate the transient changes of real operatingconditions such as disturbances, speed changes and temperaturevariations. During the training process, signatures of major faultsmust be identified, along with combinations, as the detection per-formance is strongly related to the accuracy of fault patterns.

7. Conclusion

This paper aims at developing a robust and fast response FFT-Based automatic segmentation algorithm. Due to the huge numberof fault data sets and due to the large computation cost and time ofexisting features extraction tools which are majorly based on ANN,this algorithm was developed to overcome the drawbacks of cur-rent approaches and to propose a conventional non artificial intel-ligence features selection based algorithm to the developers ofCBM applications. The suitability of acoustic emission for thedetection of machine faults of high speed centrifugal equipmentwas also assessed as a further scope of this work.

The proposed algorithm successfully identified sets of requirednumber of good features. This was carried out by identifying thesegment sizes at which all fault patterns were easily distinguished.The second training cycle made this algorithm more adaptive to AEsignature changes over time and can be considered as a furtheradvantage of this algorithm. The confidence level option gave the

control to the CBM developer to select the most appropriate valuefor the application. The computing time and detection accuracy aredirectly proportional to the confidence level. The sets of featuresselected by the proposed algorithm yielded a detection accuracyof 100% while the detection accuracy of simple classifiers is 60%.

Unlike the existing artificial intelligence based condition moni-toring tools, the proposed algorithm has a fast response andrequires a low computation cost. The short computing time neededfor segmentation and classification significantly increases thereadiness and response of the detection system and makes thiswork unique in its endeavor.

The acoustic emission proved its affordability and suitability forthe detection of faults of high speed centrifugal equipment. The AEfault patterns were found informative and the classification byspectral features proved to be a powerful tool for the fault diagnos-tic of centrifugal equipment.

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