Fan Blade Damage Neural Network

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Mechanical Systems

and

Signal ProcessingMechanical Systems and Signal Processing 20 (2006) 7893

On-line fan blade damage detection using neural networks

A.J. Oberholster, P.S. Heyns

Dynamic Systems Group, Department of Mechanical and Aeronautical Engineering, University of Pretoria,

Pretoria, Gauteng 0002, South Africa

Received 9 March 2004; received in revised form 10 September 2004; accepted 28 September 2004

Available online 24 November 2004

Abstract

This paper presents a methodology for monitoring the on-line condition of axial-flow fan blades with the

use of neural networks. In developing this methodology, the first stage was to utilise neural networks

trained on features extracted from on-line blade vibration signals measured on an experimental test

structure. Results from a stationary experimental modal analysis of the structure were used for identifying

global blade mode shapes and their corresponding frequencies. These in turn were used to assist in

identifying vibration-related features suitable for neural network training. The features were extracted from

on-line blade vibration and strain signals which were measured using a number of sensors.

The second stage in the development of the methodology entails utilising neural networks trained on

numerical Frequency Response Function (FRF) features obtained from a Finite Element Model (FEM) of

the test structure. Frequency domain features obtained from on-line experimental measurements were used

to normalise the numerical FRF features prior to neural network training. Following training, the networks

were tested using experimental frequency domain features. This approach makes it unnecessary to damage

the structure in order to train the neural networks.

The paper shows that it is possible to classify damage for several fan blades by using neural networks

with on-line vibration measurements from sensors not necessarily installed on the damaged blades

themselves. The significance of this is that it proves the possibility to perform on-line fan blade damage

classification using less than one sensor per blade. Even more significant is the demonstration that an on-

line damage detection system for a fan can be developed without having to damage the actual structure.r 2004 Elsevier Ltd. All rights reserved.

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www.elsevier.com/locate/jnlabr/ymssp

0888-3270/$ - see front matterr 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.ymssp.2004.09.007

Corresponding author. Tel.: +27 12 420 3288; fax: +27 12 362 5087.

E-mail address: [email protected] (A.J. Oberholster).
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1. Introduction

A number of power stations use axial-flow forced-draught and induced-draught fans to

facilitate airflow through coal-fired boilers. Some of these fans blade attachment shafts are prone

to failure during operation, resulting in damage and production losses. Hence, the need wasidentified for the on-line condition monitoring of fan blades for the purposes of locating and

quantifying damage. Conventional vibration-monitoring methods are inadequate for these

purposes.

One solution is to place a vibration sensor on each fan blade for on-line measurements. Heyns

and Smit [1] present an approach to on-line damage detection of fan blades, based on the

measurement of frequency shifts. The sensors they use include strain sensors and an

accelerometer. They use AutoRegressive Moving Average with eXogenous signal (ARMAX)

models to determine natural frequencies from the output-only blade vibration data measured

using one sensor per blade. Heyns and Smit note that the sensors should be mounted directly on

the fan blades at carefully chosen locations in order to provide useful information about the levelsof damage in the blades.

Another approach to on-line condition monitoring of blades is to use shaft torsional vibration

data. Maynard and Trethewey[2]demonstrate the feasibility of detecting the changes in a blades

modal frequencies resulting from blade cracks by looking at the frequency shifts in the spectral

data of shaft torsional vibration signals. The prerequisite they lay down for being able to do so, is

that the specific blade modes of interest must couple with torsion. In other words, these blade

modes must be of a global nature involving torsional shaft motion.

The use of neural networks trained with casing vibration signal features is another on-line

technique for monitoring global damage. Boek et al.[3]perform on-line condition monitoring on

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Nomenclature

a beam excitation force positionbb beam width

c1, c2 feature normalisation constantsD blade damage indicatorFA, FB, FC excitation forces

H1(o) finite element model transfer functionH2(o) ARMAX model transfer function

h beam heightl beam length

o1, o2 feature normalisation frequency limits

om modal frequency

w blade #1 damage levelx blade #2 damage level

y blade #3 damage levelz blade #4 damage level

A.J. Oberholster, P.S. Heyns / Mechanical Systems and Signal Processing 20 (2006) 7893 79


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a desktop fan for different fault scenarios, such as load imbalance, shaft imbalance and blade

cracks, using vibration measurements from two accelerometers mounted radially and axially on

the fans motor cover. A neural network is trained on the features extracted from the spectral data

of the sensors. Good results are obtained for distinguishing between physically different faultssuch as an imbalance versus a cracked blade.

The questions therefore arise whether the condition of fan blades can be monitored by using a

technique that combines local blade and global structural vibration-monitoring techniques, and

whether this can be done by using less than one sensor per fan blade. To answer this question, a

methodology was developed in two stages involving neural networks. In the first stage, vibration

signal features obtained from experimental testing were used for neural network training. In the

second stage of the methodology development, the question of whether it is possible to train

neural networks using FEM calculations is addressed. This will show whether it is possible to

employ neural networks for on-line blade condition monitoring without having to incur damage

on the experimental structure.

2. Experimental test structure

The Fan Blade Condition Monitoring Test Structure (FaBCoM TeSt) is in essence an

overhung-rotor assembly with a hub on which four straight rectangular blades are mounted. A

three-dimensional computer drawing of the assembly is shown in Fig. 1. The length of each blade

is 365.5 mm, the diameter of the hub 250 mm and the total rotor diameter is about 1.023 m. Except

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Fig. 1. Three-dimensional computer drawing of the FaBCoM TeSt.

A.J. Oberholster, P.S. Heyns / Mechanical Systems and Signal Processing 20 (2006) 789380


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for the aluminium hub, the rest of the structure consists of mild carbon steel components. The

rotor is driven via fan belt transmission by a variable speed motor controller.

3. Preliminary investigation: global mode shape frequency sensitivity analysis

The purpose of the sensitivity analysis was firstly, to investigate the feasibility of quantifying

and qualifying blade damage in a fan assembly by making use of the frequency shifts of global

mode shapes. Secondly, it was used for identifying certain Global Mode Shape Frequencies

(GMSFs) that might be useful in damage detection of the FaBCoM TeSt.

The influences of several operational variables on GMSF shift were studied in order to identify

GMSFs that would not only be insensitive to these variables, but would also be good indicators of

the location and extent of the damage. The variables used in the analysis included temperature,

accumulated debris mass and rotational speed. Different scenarios of damage in terms of thenumber and position of cracked blades were also considered. The sensitivity analysis was

performed by using the modal analysis results of an FEM of the FaBCoM TeSt for each of the

different variables and then comparing the obtained GMSFs for each set of results. All numerical

calculations were done for a temperature of 25 1C and a rotational speed of 750 rpm except where

stated differently. A more in-depth discussion of the FEM is given in the second stage of the

methodology development.

The ideal GMSF would be one of which the frequency shift is independent of operational

variables, such as rotational speed and temperature, but at the same time is an excellent quantifier

and qualifier of blade damage. This means that the GMSF should be sensitive to a specific damage

scenario to allow the easy detection of damage for that particular damage scenario. The

implication of this is that a different GMSF that is indicative of damage will have to be identifiedfor each damage scenario considered.

Typical results obtained are shown inFig. 2for the numerical mode at about 421 Hz. It can be

seen that the frequency of this mode is insensitive to temperature and rotational speed. Although

this particular GMSF is sensitive to blade damage, it cannot be directly used for distinguishing

between different damage scenarios as similar results are yielded. This is clearly shown inFigs. 2c

and d, which are the results for adjacent positioned and opposite positioned damaged blades,

respectively. It can be concluded from the results yielded by this numerical GMSF sensitivity

analysis that it is feasible to quantify and qualify the blade damage of a fan assembly by making

use of GMSF shifts.

4. First stage: neural network training based on experimental measurements

4.1. Experimental modal analysis of the FaBCoM test

One of the aims of the Experimental Modal Analysis (EMA) was to determine whether the

FaBCoM TeSt satisfies the condition set by Maynard and Trethewey [2] for the use of shaft

torsional vibration measurements for blade damage detection. Also, the EMA was used for

identifying the natural frequencies of the structure to be monitored during experimental testing.

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Throughout the EMA testing, the pulley end of the FaBCoM TeSt shaft was rotationally

constrained.Vibration measurements were taken in three planes at 12 points on each blade using

piezoelectric accelerometers and two Siglabs signal analysers. Excitation of the FaBCoM TeSt

was provided at a single point on one of the blades, using a Modal 50 Dynamic Shaker driven

with a white-noise signal generated by one of the Siglabs signal analysers. The point of excitation

was instrumented with an impedance head, allowing the force input and acceleration signals to be

measured at that point.

The Structural Dynamics Toolbox Version 3s for Matlabs [4]was used to extract mode shapes

from the measured data. Seventeen natural frequencies were identified within a 5 kHz bandwidth.

The fourth mode shape identified at about 361 Hz is shown in Fig. 3 and consists of a sideways

vibration of the blades, coupled with torsion. Therefore, the FaBCoM TeSt is suitable for usingtorsional vibration measurements to detect blade damage.

4.2. Experimental testing procedure

Experimental testing was conducted at a fixed rotational speed of 750 rpm of the rotor and

normal atmospheric conditions. During experimental testing, measurements were taken on three

of the four blades by means of piezoelectric strain sensors positioned at the trailing edges of the

roots of these three blades. These were used to measure the lengthwise strain on the blades while a

piezoelectric accelerometer was installed on the remaining blades fitting in order to measure

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Fig. 2. Typical sensitivity analysis results.



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torsional vibrations.Fig. 4shows a diagram of the positions and orientations of the transducers as

well as the locations where blade root damage was induced with hacksaw cuts during testing.

Blade damage is defined in this paper as the length of the crack in the blade root, expressed as a

percentage of the total blade width. A number of damage cases with different damage levels were

considered during testing mainly on blades # 3 and #4, and also on blade #2.

Damage levels of the blades will be described by the subscripts of a damage indicator as Dw,x,y,z,

where w, x, y and z indicate the damage levels of blades #1, #2, #3 and #4, respectively.

A linear scale of 15 was used with 1 being a 0% damage level and 5 a 50% damage level as giveninTable 1. For example, a damage indicator ofD1,3,5,5means that the damage levels of the blades

are 0%, 25%, 50% and 50% for blades #1, #2, #3 and #4, respectively.

The signals were transmitted from the transducers by means of a slip ring assembly to charge

amplifiers for signal conditioning. For each different damage level considered, continuous time

measurements were taken from all the signals using two four-channel Siglabs signal analysers.

The record length used was 192 s with a measurement bandwidth of 2000 Hz. Each record was the

split up into 24 sub-records or samples of 8 s each.

4.3. Experimental neural network training

Two neural networks were trained using the Neural Network Toolbox for Matlabs [5] with

supervised training. The networks were trained to detect damage on blades #3 and #4, using the

network architecture of Boek et al.[3]as a guideline. Network architecture with two hidden layers

yielded the best results. The networks were trained for a performance goal of 0.001 and a learning

rate of 0.01.

Felber and Ventura [6] define the Modal Ratio Function (MRF), which they used for

determining the mode shapes of the Queensborough Bridge Main Span from ambient vibration

data. This function is in essence a transmissibility function with respect to a certain reference

signal and makes use of user-defined phase and coherence parameters to identify likely modal

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Fig. 3. Fourth mode shape at 361 Hz.



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frequencies. Instead of the ARMAX models as used by Heyns and Smit[1], the MRF was used in

this stage of the methodology development for extracting modal information from measured data.

Compared to ARMAX models, the MRF is far more cost-effective with regards to computational

time even though being less accurate at the same time.Fig. 5ashows the changes in MRF curves

damage cases D1,1,1,1, D1,1,3,3 and D1,1,5,5 over a frequency range of 450495 Hz.

Features were also extracted from PSDs of the measured signals as shown in Figs. 5b and cforthe same damage cases considered in Fig. 5a.Fig. 5b shows the change in area underneath the

PSD curves over a certain frequency range whileFig. 5cshows the shift in peak frequencies over

another frequency range. A total of 11 features were identified to be useful for neural network

training.

In order to reduce the input vector dimension to the networks, principal component analysis

was performed on these features. Principal component analysis basically has three effects namely

input vector orthogonolisation, orthogonolised component sorting and component elimination

[5]. In other words, the technique first orthogonolises the input vector so that its components are

uncorrelated. It then sorts the resulting components so that those with the largest variation come

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Table 1

Damage-level scale

Scale of damage level Actual damage level (%)

1 02 12.5

3 25

4 37.5

5 50

Sensor #3

Sensor #2

Sensor #1

Accelerometer

Blade #1

Blade #4

Blade #2

Blade #3

Blade Damage

Fig. 4. Measurement locations schematic.



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first. After that, components that contribute least to the data set variation are eliminated. This

caused the input vector dimension to be reduced from 11 1 to 5 1.

The networks were trained on the features of 20 of the 24 sub-records of the different damagecases. The first network (Network #1) was trained using the features extracted from the

accelerometer signal and strain sensor signal from blade #1. Another network (Network #3) was

also trained using the accelerometer signal features, but instead of blade #1 strain sensor signal

features those from blade #3 were used.

4.4. Results

Fig. 6indicates that Network #1 and Network #3 yield excellent results whereas Network #2

yields poor results. Network #2 is in actual fact Network #1 which was tested on the features

obtained from measurements on blade #2. The testing results indicate that it is necessary to train aneural network for each different strain sensor location used. The results from Network #1 and

Network #3 prove that it is possible to accurately quantify and qualify blade damage on an

operating fan using neural networks trained on experimental features.

5. Second stage: neural network training based on numerical data

Following the excellent results yielded by experimental neural network training, it was decided

to investigate the possibility of training neural networks on features obtained from FEM

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Fig. 5. Experimental feature identification.



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calculations. In the first stage of developing the methodology of concern, a decision was taken tomake use of MRFs for modal parameter extraction instead of ARMAX modelling, owing to

computational costs. However, the results of MRFs were found to be far less accurate than the

results of ARMAX models. Also, once an ARMAX model has been estimated for a time signal, it

is really easy to obtain modal frequencies for that model. Consequently, ARMAX models were

used in this stage with model orders of 48.

5.1. Finite element modelling

An initial FEM of the FaBCoM TeSt, as shown in Fig. 7along with the numerical excitation

force used during testing, was constructed using assumed material properties. Structural dampingis ignored in this model and the entire model consists of solid elements except for the blades,

which are composed of shell elements. The shell elements are connected to the solid elements using

Multi-Point Constraints (MPCs). The boundary conditions at the bearing and pulley positions

were approximated to match those of the experimental setup. This was accomplished by

constraining the displacement of the appropriate nodes to zero in the radial and axial directions.

It is this initial FEM that was used for the GMSF sensitivity analysis.

Following this preliminary investigation, the validity of the initial FEM was studied using

Modal Assurance Criterion (MAC) matrix calculations and results from the EMA of the

FaBCoM TeSt. Based on MAC matrix results, a decision was taken to update the FEM by

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Fig. 6. Experimental supervision network results.



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updating the material properties within the FEM and by tuning the first natural torsional

frequency of the FEMs shaft to that of the FaBCoM TeSt. To tune this frequency, the solid

element shaft of the FEM was replaced by a beam element shaft. This enabled an easy adjustment

of the shaft diameter for tuning purposes. A structural damping coefficient of 0.01 is assumed for

the updated model. Using the updated FEM, better MAC matrix results were obtained showingthe updated FEM to be a more accurate representation of the FaBCoM TeSt than the initial

FEM.

5.2. Numerical testing

Rotational stiffening effects were taken into account in the FEM for a constant rotational speed

of 750 rpm, as considered during experimental testing. The FEM was then tested for increments of

25% in blade root damage as allowed by the blade element mesh resolution in the model. As

during experimental testing, damage is simulated at blades #3 and #4. Deletions of appropriate

MPCs were used for damage simulation, which is similar to the nodal dissociation method used byHeyns and Smit[1]for crack modelling.

The point of excitation (identical to the one shown inFig. 7) corresponds to the position of the

excitation point used in the EMA and allows torsional, axial and tangential excitation of the

blades. FRFs of the FEM were calculated at 2.5 Hz intervals over a bandwidth ranging from 2.5

to 2000 Hz. At the time of calculation of these FRFs it was assumed that, by only looking at

frequency-related FRF characteristics, a sufficient number of features would be generated for

neural network training purposes. However, during initial neural network training, this

assumption was proved to be invalid. For this reason, it was decided to identify amplitude-

related FRF features as well.

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Fig. 7. Initial FEM of the FaBCoM TeSt showing the numerical excitation force.



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This introduced a new problem, as the excitation force used in the FEM FRF calculations is not

representative of the forces exerted on the FaBCoM TeSt during operation. This is because in the

FEM, a single concentrated force on a single blade is used while the operational forces exerted on

FaBCoM TeSt are much more complicated forces distributed over the lengths of all the blades.Due to these differences in excitation, the FEM FRFs differ from the estimated FRFs yielded by

the ARMAX models. This phenomenon is shown mathematically for a cantilever beam when

different harmonic excitation forces with regard to position and distribution are considered. Two

of the three forces considered for this demonstration are concentrated loads at the end of the

beam and at quarter length of the beam, respectively, while another uniform distributed load

along the length of the beam is also considered. Calculating the vertical responses of the beam tip

to the different forces using continuous beam calculations [7], the transfer function graphs are

obtained as shown inFig. 8. The difference in transfer function amplitudes is explained by the fact

that the three transfer functions are completely different from one another.

Normalisation techniques are used to address this issue of the amplitude differences betweentransfer functions. The main assumption used in the normalisation is that, as shown inFig. 9, the

FEM and ARMAX FRF curves will have a constant difference in amplitude over a certain

frequency range of interest, ranging from o1 to o2.

To express this mathematically, let H1(o) and H2(o) be an experimental ARMAX model FRF

and a numerical FRF, respectively, at an arbitrary frequency within a bandwidth ranging from o1to o2, so that

logH1 logH2 c1

logH1 logH2 c1

log H1H2

c1

)H1

H2 10c1 : 1

Forn discrete points over this frequency range, the area underneath the curve H1over the range

is given by

Xni1

logHi1 logH11 logH

21 . . . logH

n1 logH

11 H

21 . . . H

n1: (2)

This can also be written for H2.Using Eq. (1) and assuming c1 remains constant over the specified range, Eq. (3) is obtainedXn

i1

logHi1 logH12 10

c1 H22 10c1

. . . Hn2 10c1 log10nc1 H12 H

22 . . . H

n2

logH12 H22 . . . H

n2 nc1

c1 1

n

Xni1

logHi1 Xni1

logHi2

" #: 3

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Fig. 9. Normalisation assumption.

Fig. 8. Cantilever beam transfer functions comparison.



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The second normalisation step used involves the simple factoring of the FRF areas as given in

Eq. (4). In other words, the normalisation constant c2 is the ratio of the total sum of the

logarithmic values ofH1 to that ofH2 over the bandwidth of interest

c2

Pni1

logHi1

Pni1

logHi2

: (4)

The frequency ranges used were chosen to be 10 Hz around each modal frequency (om) for the

undamaged case as given in Eq. (5):

o1 om 5 Hz and o2 om 5 Hz: (5)

To obtain the final normalised energies, these two normalisation steps were combined as given

for a frequency range by Eq. (6):

Normalized energy 1

2

Xni1

logHi2 nc1

! c2

Xni1

logHi2

!" #: (6)

When the frequency resolution of 2.5 Hz is used together with the frequency range definition

given by Eq. (5), the number of discrete points in these ranges, n; will be equal to five.

5.3. Numerical neural network training

Marwala[8] shows that better results are obtained from a committee of networks than from

using a single neural network for a particular problem. For this reason, use is made of committees

of networks for five different damage detection approaches, namely for detecting the level of

global blade damage, the damage levels of multiple blades, the sensor position, the level of blade

#3 damage and the level of blade #4 damage. The committees consist of four networks, each with

different architectures employing Linear Transfer Functions (LTFs) and Tan-Sigmoid Transfer

Functions (TSTFs). The different committees with the architecture of each network are listed in

Table 2.

Three sets of training data were used, containing features of the FEM strain FRFs of blades #1,

#2 and #3, respectively, as well as from the rotational FEM acceleration FRFs at the root of blade#4 for the different damage cases considered. These features included natural frequency shifts and

energies at a certain frequency for both the strain signal of the particular blade and the rotational

acceleration signal.

5.4. Results

Some of the results of the network testing are shown in Figs. 10 and 11. As can be seen, very

good results are obtained for detecting damage to blade #3. The results obtained for detecting

damage to blade #4 are not quite as good. The results for detecting global blade damage are

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poorer than those for blade #3, but remain useful. It is clear that the network committee trained

for sensor position identification does not yield useful results. The network committee for

detecting damage on all four blades simultaneously yields very good results for blade #3 but the

results for blade #4 are not as good. The good results for blades #1 and #2 damage detection by

this committee could be expected as no damage to these two blades were considered.

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Table 2

Committees of networks

Committee Global blade damage

Network number 4 5 6 7

Dimensions 12 1 11 1 10 1 9 1

Transfer function

Layer 1 TSTF TSTF TSTF TSTF

Layer 2 LTF LTF LTF LTF

Layer 3

Layer 4

Committee Multiple blade damage


Dimensions 64 12 4 12 64 12 104

Transfer function


Layer 2 LTF LTF TSTF TSTFLayer 3 LTF LTF

Layer 4

Committee Blade identification


Dimensions 681 6 84 1 12 1 10 1

Transfer function


Layer 2 TSTF TSTF LTF LTF

Layer 3 LTF TSTF

Layer 4 LTF

Committee Blade #3 damage

Network number 18 19 20 21Dimensions 12 62 1 12 1 12 61 12 621

Transfer function


Layer 2 TSTF LTF TSTF TSTF

Layer 3 TSTF LTF TSTF

Layer 4 LTF LTF

Committee Blade #4 damage


Dimensions 12 1 12 62 1 6 62 1 6 821

Transfer function


Layer 2 LTF TSTF TSTF TSTF

Layer 3 TSTF TSTF TSTF

Layer 4 LTF LTF LTF



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Fig. 10. Various damage detection results.

Fig. 11. Multiple blade damage detection results.



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6. Conclusions

Two stages were involved in developing a methodology for the on-line detection of damage to

multiple blades on a fan. In the first stage, use was made of experimental neural network trainingand this training yielded excellent results. Adopting this experimental approach, neural networks

were trained for different sensor positions with regards to damage location. This was

accomplished using features from one strain sensor signal and one rotational acceleration signal

for each network. These networks were shown to have the ability to quantify and qualify blade

damage accurately on a four-bladed experimental structure. It is desirable to use this approach in

cases where there is ready access to an experimental structure, or where measuring the damage on

an operational structure will not incur heavy costs.

In the second stage, acceptable results were yielded for neural network committees trained with

numerically calculated features. Here the neural networks trained as in the first stage with the

difference of using features obtained from strain and acceleration FRFs calculated numericallyfrom the FEM of the experimental structure. Normalisation of these features was performed in

order to compensate for the differences in testing of the FEM and the experimental structure. This

was accomplished using constants calculated from a single set of experimental features obtained

from an undamaged structure. The neural networks committees used were found to be capable of

detecting multiple blade damage, although not as accurately as in the experimental approach. This

is due to a number of assumptions included in the FEM. The numerical approach is preferable to

the experimental approach where it is less costly to construct, update and test an FEM than to test

an experimental or operational structure by means of damage simulation.

References

[1] P.S. Heyns, W.G. Smit, On-line vibration monitoring for detecting fan blade damage, Proceedings of the 14th

International Congress of COMADEM, 2001, pp. 681687.

[2] K.P. Maynard, M. Trethewey, On the feasibility of blade crack detection through torsional vibration

measurements, Proceedings of the 53rd Meeting of the Society for Machinery Failure Prevention Technology,

1999, pp. 451459.

[3] M.J. Boek, J.L. Cybulski, A.S. Szczepanik, Embedding neural networks in on-line monitoring applications,

ANZIIS 1993, pp. 172176.

[4] E. Balme` s, Scientific software group, Experimental and Analytical Structural Dynamics Toolbox Version 3 Users

Guide, 1997.

[5] H. Demuth, M. Beale, Neural Network Toolbox (Version 4) For Use with Matlab, The Math Works Inc. 2001.

[6] A.J. Felber, C.E. Ventura, Frequency domain analysis of the ambient vibration data of the queensborough bridgemain span, Proceedings of the 14th IMAC, 1996, pp. 459465.

[7] S.S. Rao, Mechanical Vibrations, third ed, Addison-Wesley Publishing Company, Reading, MA, 1995.

[8] T. Marwala, Damage identification using committee of neural networks, Journal of Engineering Mechanics (2000)

pp. 4350.

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Fan Blade Damage Neural Network

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