artificial neural network based fault diagnostics of rolling element bearings using time-domain...

5/24/2018 Artificial Neural Network Based Fault Diagnostics of Rolling Element Bearings Using TIME-DOMAIN FEATURES

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Mechanical Systems and Signal Processing (2003) 17(2), 317328

doi:10.1006/mssp.2001.1462, available online at http://www.idealibrary.com on

ARTIFICIALNEURALNETWORKBASED FAULT

DIAGNOSTICSOFROLLINGELEMENTBEARINGS

USINGTIME-DOMAINFEATURES

B. Samanta and K. R. Al-Balushi

Department of Mechanical and Industrial Engineering, College of Engineering,Sultan Qaboos University, P.O. Box 33, PC 123, Muscat, Sultanate of Oman

(Received 4 March 2000, accepted 17 September 2001)

A procedure is presented for fault diagnosis of rolling element bearings through artificialneural network (ANN). The characteristic features of time-domain vibration signals of therotating machinery with normal and defective bearings have been used as inputs to theANN consisting of input, hidden and output layers. The features are obtained from directprocessing of the signal segments using very simple preprocessing. The input layer consistsof five nodes, one each for root mean square, variance, skewness, kurtosis and normalisedsixth central moment of the time-domain vibration signals. The inputs are normalised inthe range of 0.0 and 1.0 except for the skewness which is normalised between 1.0 and 1.0.The output layer consists of two binary nodes indicating the status of the machine}normalor defective bearings. Two hidden layers with different number of neurons have been used.The ANN is trained using backpropagation algorithm with a subset of the experimentaldata for known machine conditions. The ANN is tested using the remaining set of data.

The effects of some preprocessing techniques like high-pass, band-pass filtration, envelopedetection (demodulation) and wavelet transform of the vibration signals, prior to featureextraction, are also studied. The results show the effectiveness of the ANN in diagnosis ofthe machine condition. The proposed procedure requires only a few features extracted fromthe measured vibration data either directly or with simple preprocessing. The reducednumber of inputs leads to faster training requiring far less iterations making the proceduresuitable for on-line condition monitoring and diagnostics of machines.

# 2003 Elsevier Science Ltd. All rights reserved.

1. INTRODUCTION

The use of vibration signals is quite common in the field of condition monitoring anddiagnostics of rotating machinery[17].Detection of machine faults like mass unbalance,

rotor rub, shaft misalignment, gear failures and bearing defects is possible by comparing

the vibration signals of a machine operating with and without faulty conditions. These

signals can also be used to detect the incipient failures of the machine components,

through on-line monitoring system, reducing the possibility of catastrophic damage.

The presence of a variety of noise and a wide spectrum of bearing defect signals poses

difficulty in the detection of bearing condition using time-domain vibration signals. This

necessitates an approach for identification and quantification of the characteristic features

relevant to the bearing conditions[79].The procedures involved multi-stage processing of

the vibration signal, e.g. high-pass or band-pass filtering, demodulation, low-pass filteringof the demodulated signal and finally processing to extract the desired characteristic

features.

Although often the visual inspection of the frequency-domain features of the vibration

signals is adequate to identify the faults, there is a need for a reliable, fast and automated

08883270/03/+$30.00/0 # 2003 Elsevier Science Ltd. All rights reserved.


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procedure of diagnostics. Artificial neural networks (ANNs) have potential applications in

automated detection and diagnosis of machine conditions [16], machining operations

[10, 11],industrial processes[12]and machine tool coolant system [13].Many of the ANNs

for machine condition monitoring used the preprocessed frequency-domain features of the

measured vibration signals. In [4], a two-stage ANN based on vibration spectra was

proposed for diagnosis of rolling element bearings. Wavelet transforms were proposed aspreprocessor in conjunction with ANNs in [5]. Model-based procedures using time-

domain signals were proposed in [3, 14].

In[1, 2],the time-domain vibration signals in two orthogonal directions representing the

orbit plot of the shaft centre were used to obtain the probability densities of vibration

magnitude, its time derivative and time integral. Higher-order moments of these

distributions were computed and used as inputs to the multi-layer ANNs for detection

of mass unbalance and rotor rub in a rotating machine. Although the success rate of both

training and test was reasonably high (98100%), the procedure needed a relatively large

number of training epochs (30005000).

In the present work, a procedure is presented for bearing fault diagnostics using time-domain features and ANN with fast training capability. The vibration signals obtained

from a group of sensors are subjected to direct and simple processing for extraction of

features that are subsequently used as inputs to the ANNs for diagnosing bearing

condition of a rotating machine. In the present approach, sets of normalised features are

used so that even if the signals change in magnitude due to the change in speed or quality

of sensor mounting, the diagnostic results are unaffected as long as the signal patterns

remain unchanged. The features are obtained from the segments of the measured vibration

signals instead of single values like crest factor, kurtosis and peaks for the undivided

signals [79]. The effects of different sensor locations, simple preprocessing like high- or

band-pass filtering, envelope detection (demodulation) and somewhat complex signalprocessing like wavelet transform are studied. The training speed of ANN, requiring

epochs less than 50, is enhanced using the relevant features of the signals characterising the

bearing conditions. The procedure is illustrated using the vibration data of a submersible

pump with normal and defective bearing[15].

2. VIBRATION DATA

Reference [15] presented the measurements from two ring and one triaxial accelero-

meter on a one-channel impeller submersible pump driven by an electrical motor.

The impeller shaft was mounted in pump casing using a single ball bearing on theupper end and a double bearing on the lower end near the impeller, refer to [15] for

details. The first accelerometer was placed on top of the pump casing to measure

axial vibration. The other ring accelerometer was placed near the single bearing for

measuring radial vibration. The triaxial accelerometer was placed near the double

bearing to measure vibration in three orthogonal directions. Separate measurements

were obtained for normal and defective bearing at the upper end with outer race

defect. The location of sensors and the direction of acceleration measurement were as

follows: sensor 1 (top of casing}axial), 2 (upper bearing}radial), 3 (lower bearing}

transverse), 4 (lower bearing}radial) and 5 (lower bearing}axial). These were connected

to channels (15) of a digitiser. The samples were taken at 51.2 kSa/s for each of the fivechannels for a rotational speed of 1125 rpm (18.75 Hz). The number of samples collected

for each channel was 20 480 for a time of 0.40 s or 7.5 cycles. In the present work, these

time-domain data have been preprocessed to extract the features for using as inputs to the

ANN.

B. SAMANTA AND K. R. AL-BALUSHI318


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3. FEATURE SELECTION

One set of experimental data each with normal and failed bearing was presented in[15].

For each set, five vibration signals consisting of 20 480 samples (yi) were obtained using

accelerometers to monitor the machine condition. In the present work, these signals, after

removing the d.c. offset, have been divided into 20 bins of 1024 (n) non-overlapping

samples each. Each of these bins has been processed using MATLAB [16]to extract thefollowing five features: root mean square (rms), variance (s2), skewness (normalised third

central moment, g3), kurtosis (normalised fourth central moment, g4) and normalised sixth

central moment (g6) as follows:

%yyi yi m, the mean is estimated as: m Efyig,

rms

ffiffiffiffiffiffiffiffiffiffiffiPyy2i

n

s

s2

Ef yy2

ig;

g3

Ef yy3ig

s3 ;

g4

Ef yy4ig

s4 ;

g6

Ef yy6ig

s6

where Erepresents the expected value of the function.

Figure 1shows the plots of features extracted from the vibration signals of five channels,

where each row represents the features for one signal. Of these 25 plots, all are normalised

in the range of 0.01.0, except the third column (skewness) normalised in the range of1

1. For normalisation, each feature was divided by the corresponding maximum value. The

plot of variance (second column) shows better separation between the normal and the

defective cases than the plot of rms (first column), especially when the corresponding

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Figure 1. Time-domain features of acquired vibration signals:}, normal; ....., defective.

ANN-BASED BEARING FAULT DIAGNOSTICS 319


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signal rms values are not very close, since for a zero-mean signal the variance is the square

of its rms value. This justifies the use of both rms and variance in the diagnosis process.

The features show some difference between the cases with normal or faulty bearing,

especially for signals 24, thus making features extracted from these signals suitable for

fault diagnosis. The variations of features of different signals with or without the bearing

fault were used for training the ANN and diagnosing the bearing condition. Thecontribution of the features and the signals in the diagnosis of machine condition is

discussed in the following sections.

4. TRAINING OF ARTIFICIAL NEURAL NETWORK

The neural network consists of the input layer, two hidden layers and the output layer,

Fig 2.The input layer has nodes representing the normalised features extracted from the

measured vibration signals. The number of neurons in the first hidden layer was varied

from 10 to 30 and the second one, from 5 to 10. The number of output nodes was varied

between 1 and 2. The target values of two output nodes can have only binary levelsrepresenting normal and failed bearing. The inputs were normalised in the range of 0.0 and

1.0 except for skewness for which the range was 1.01.0. The ANN was trained and

implemented using the MATLAB neural network toolbox using backpropagation with

LevenbergMarquardt algorithm[16].For training, a target mean square error (MSE) of

1010, a minimum gradient of 1010 and maximum iteration number (epoch) of 5000 were

used. The training process would stop if any of these conditions were met. The initial

weights and biases of the network were generated automatically by the program.

5. DIAGNOSIS OF BEARING CONDITION

The vibration data representing 15 signals each with 20 480 samples were preprocessed

to obtain 20100 sets of five normalised input features each for normal and failed bearing.

Of these total 40200 sets first 24120 sets consisting of 1260 each for normal and failed

bearing were used to train the network. The remaining 1680 sets of input features were

used for testing. The structure of the ANN giving best results was 5:16:10:2 where the

figures represent the numbers of nodes in the input, the first hidden, the second hidden and

the output layers, respectively. In the training stage, the target value of the first output

node for the normal bearing condition was set 1 and that for the failed bearing was set 0,

similar toFig. 3(a).Similarly, the target value for the second output node for the normalbearing was set 0 and that for the failed bearing was 1, similar toFig. 3(b).

Figure 2. Artificial neural network.



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5.1. EFFECTS OF INPUT SIGNALS

Table 1shows the results of training and testing the diagnostic capability of the ANN

for different input signals 15, both individually and groups. All five features were used for

studying the roles of the input signals. Though each of the individual input signals gave

rise to 100% training success, the test success varied from 93.75% (signals 2, 4) to 62.50%

0 20 40 60 80 100 120

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Targets/netoutputs

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Sequence no: 1_72 training data 73_120 test data

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Figure 3. Targets and actual values of output nodes: (a) node 1, (b) node 2: . . .. . .. . ., targets; }, outputs.



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(signal 1). The signals with most significant contributions in identification of the bearing

condition were found to be 2, 4 and 3 with test success of 15/16 (93.75%), 15/16 (93.75%)

and 13/16 (81.25%), respectively. These signals correspond to the radial vibration near the

failed bearing (signal 2), radial vibration at the double bearing (signal 4) and the transverse

vibration at the double bearing (signal 3) which were influenced by the bearing fault. The

relatively low test success of signals 1 and 5 are due to the fact that the outer race damage

of the ball bearing would not have significant effect on the axial vibration of the pump at

these locations. The groups of signals, 2, 3 (case 6), and 24 (case 7) lead to 100% success

in both training and test making these suitable for diagnostics. This also shows the

importance of using more than one signal in the diagnostic process. However, whensignals 1 and 5 were used along with signals 24 (cases 8 and 9), the training was

terminated after some iterations (epochs), due to the minimum gradient criterion of the

algorithm[16]. Both training and test success were low confirming the earlier observations.

5.2. EFFECTS OF SIGNAL FEATURES

Table 2shows the relative importance of signal features in the identification of machine

condition. For all the cases, three input signals (2,3,4) were used with training success of

100%. The use of four signal features, namely, variance (s2), skewness (g3), kurtosis (g4)

and normalised sixth central moment (g6) gave 100% success in both training (72/72) andtest (48/48), case 1. In cases 24, only three features were used and test success was 47/48

(97.92%). In these, sixth moment, g6, was common. However, when g6 was omitted from

the input list (case 5), the test success dropped from 100 to 85.42% and the training was

completed after 177 epoch. The use of rms did not affect the results for the signals (24)

without filtration. However, for all subsequent analysis rms was included in the list of

features because of its positive effect on the ANN-based diagnostic process. The use of

central moments of order more than six did not have any significant effect on the diagnosis

results.

Figures 3(a)and(b)show the actual values of the output nodes 1 and 2, respectively, for

both training and test sets with three input signals (24) and five input features. The actualoutputs of the nodes match quite closely with the corresponding target values.

The procedure was repeated halving the bin size from 1024 to 512 data points and using

three input signals (24) with five input features. The training success was 100% but the

test success dropped to 93/96 (96.88%).

Table 1

Effects of input signals on identification of machine condition with five features (rms, s2, g3,

g4, g6)

Case no. Input signals Training success Test success Epochs

1 1 24/24 (100%) 10/16 (62.5%) 262 2 24/24 (100%) 15/16 (93.75%) 203 3 24/24 (100%) 13/16 (81.25%) 164 4 24/24 (100%) 15/16 (93.75%) 195 5 24/24 (100%) 12/16 (75.00%) 176 2,3 48/48 (100%) 32/32 (100%) 187 2,3,4 72/72 (100%) 48/48(100%) 178 1,2,3,4 95/96 (98.96%) 55/64 (85.94%) 51y

9 1,2,3,4,5 119/120 (99.17%) 61/80 (76.25%) 63y

yThe training process was terminated due to the minimum gradient (1010) criterion[16].



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5.3. EFFECTS OF HIGH-FREQUENCY SIGNAL FEATURES

The effectiveness of the proposed features, in presence of interference signals, is

discussed in this section. The acquired vibration signals were preprocessed to obtain the

high-frequency components dominated by the bearing fault, prior to extraction of thecharacteristic features. Three schemes were studied for comparison: simple preprocessing

techniques like (i) high-pass or band-pass filtration, (ii) envelope detection or

demodulation of the band-pass filtered signal and (iii) a more elaborate signal processing

technique like wavelet transform.

5.3.1. High- and band-pass filtration

The examination of acquired vibration signals indicated the presence of low-frequency

interference. The signals were subjected to either high-pass or band-pass filtration to

remove the low-frequency interference components. Three band-pass (BP1BP3) and one

high-pass (HP) filters were studied. The band-pass frequencies (in kHz) of the BP1BP3were chosen as: BP1 (4.620.0), BP2 (4.610.0), and BP3 (2.010.0). The cut-off frequency

of the HP filter was chosen as 4.6 kHz. These frequencies were selected to cover the signal

components containing the majority of the rolling element bearing energy[17].One of the

signals (4), with normal bearings, before and after filtration (using BP1) is shown in

Fig. 4(a)and(b)and the corresponding signal, with defective bearing, is shown inFig. 4(d)

and(e). The effects of filtration on the remaining signals were similar.

Time-domain features (rms, s2, g3, g4 and g6) were obtained from each of these filtered

high-frequency signals and used in the ANN-based diagnostic procedure. Figure 5shows

the features extracted from the band-pass (BP1) filtered signals.Table 3shows the results

for different input signals and filters. The training success was 100% in all cases. The testsuccess varied from 100% (case 2) to 93.75% (case 1). The results of individual signals

were similar to that ofTable 1. The use of high-pass or band-pass filtration improved

the training and test success, especially for signals 14 and 15 in comparison with that

of signals without any preprocessing,Table 1. The features extracted from filtered signals

24, 14, 15 gave very good results (95.83100%) indicating the influence of the bearing

defect on the signal components in the selected frequency range. The results show

applicability of the proposed features for the signal components in the selected frequency

band, eliminating the effects of interfering signals outside this range.

5.3.2. Wavelet transformThe acquired vibration signals were processed through discrete wavelet transform

(DWT) using Daubechies wavelet of order 4 (Db4) at level 6 to obtain the coefficients

corresponding to approximate (A6) and details (D1D6) [16]. The low-frequency

approximate (A6) and high-frequency detail signals (D1D6) were reconstructed from

Table 2

Effects of signal features on identification of machine condition with three input signals

(2,3,4)

Case no. Input features Test success Epochs

1 s2, g3, g4, g6 48/48 (100%) 282 g3, g4, g6 47/48 (97.92%) 183 s2, g4, g6 47/48 (97.92%) 164 s2, g3, g6 47/48 (97.92%) 145 s2, g3, g4 41/48 (85.42%) 177



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Figure 4. Time-domain vibration signal 4: (a) acquired (normal), (b) band-pass filtered (normal), (c) wavelettransformed D2 (normal), (d) acquired (defective), (e) band-pass filtered (defective), (f) wavelet transformed D2(normal).

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Figure 5. Time-domain features of band-pass (BP1) filtered vibration signal 4: }, normal; ....., defective.



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these coefficients. Frequency ranges for the details were in the descending order, i.e. D1

had highest frequency content (1225.6 kHz), and D6 had lowest frequency content

(0.31.2 kHz).Figure 4(c)and(f)shows the time-domain signal of the re-synthesised detail

D2 (in the frequency range of 4.610.0 kHz) for signal 4 with normal and defective

bearing, respectively. Time-domain features, similar to that ofFig. 1,were obtained from

each of these reconstructed high-frequency signals and used in the ANN-based diagnostic

procedure. Table 4 shows the results for different input signals and high-frequencycomponents. The training success was 100% in all cases. The test success varied from

100% (case 2) to 83.33% (case 8). The features extracted from high frequency signals D2

and D3 gave very good results (97.92100%) indicating the influence of the bearing defect

on the signal components in this frequency band (2.010.0 kHz). The performance of

components (D1, D5 and D6) outside this frequency range was not satisfactory which may

be attributed to the lack of influence of bearing defect on these signal components. The

results show applicability of the proposed features for the signal components in the

selected frequency band obtained using DWT. However, the application of DWT did not

offer any substantial advantage over that of simple high-pass or band-pass filtration in

bearing condition diagnostics.

5.3.3. Demodulation

The effects of interfering signals within the selected frequency band can be minimised

by enveloping or demodulation [8, 9, 17, 18], if necessary. Demodulation or envelope

Table 3

Effects of filtered signals on identification of machine condition with five input features (rms,

s2, g3, g4, g6)

Case no. Input signals Filtery Training success Test success Epochs

1 2, 3 BP1 48/48 (100%) 30/32 (93.75%) 192 2, 3, 4 BP1 72/72 (100%) 48/48 (100%) 193 1, 2, 3, 4 BP1 96/96 (100%) 63/64 (98.44%) 174 1, 2, 3, 4, 5 BP1 120/120 (100%) 77/80 (96.25%) 265 2, 3, 4 BP2 72/72 (100%) 46/48 (95.83%) 206 2 , 3, 4 BP3 72/72 (100%) 47/48 (97.92%) 347 2 , 3, 4 HP 72/72 (100%) 46/48 (95.83%) 128 1, 2, 3, 4, 5 HP 120/120 (100%) 78/80 (97.50%) 21

yFrequencies (kHz): BP1 (4.620.0); BP2 (4.610.0); BP3 (2.010.0); HP (4.6).

Table 4

Effects of wavelet transformed signals on identification of machine condition with five inputfeatures (rms, s2, g3, g4, g6)

Case no. Input signals Details Training success Test success Epochs

1 2, 3, 4 D1 72/72 (100%) 42/48 (87.50%) 202 2, 3, 4 D2 72/72 (100%) 48/48 (100%) 263 2, 3 D2 48/48 (100%) 31/32 (96.88%) 184 1, 2, 3, 4 D2 96/96 (100%) 63/64 (98.44%) 225 1, 2, 3, 4, 5 D2 120/120 (100%) 77/80 (96.25%) 156 2 , 3, 4 D3 72/72 (100%) 47/48 (97.92%) 147 2 , 3, 4 D4 72/72 (100%) 45/48 (93.75%) 18

8 2, 3, 4 D5 72/72 (100%) 40/48 (83.33%) 279 2, 3, 4 D6 72/72 (100%) 41/48 (85.42%) 34



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detection makes the diagnostic process a little more independent of a particular machinesince it focuses on the low-amplitude high-frequency broadband signals characterising the

bearing condition. Another property of a demodulated signal is that it signifies local fault

as it gets easily attenuated and does not travel well through the machine structure because

of its high frequency. It is important to locate the accelerometers as close as feasible to

the bearing under test to keep the measurement path as short as possible. However,

the dependence of diagnostic results on measurement point is not significant due to the

normalisation process used for feature extraction in the present study, as long as the

measured vibration signals are influenced by the bearing condition. In the present study,

the envelope of each band-pass (BP1) filtered signal was obtained as the amplitude of a

complex signal consisting of the real signal and the corresponding Hilbert transform as theimaginary part[16, 18]. An analog rectification and smoothing before sampling could be

considered as an alternative option. However, the demodulation using digital processing is

preferred because of its greater flexibility compared to its analog form. The features

extracted from the envelopes, similar to that ofFig. 1,were used as inputs to the ANN and

the results obtained are shown in Table 5. The training success for each was 100%.

The results are almost similar to that of original signals (Table 1), especially for cases 24,

6 and 7 with some improvements in cases 1 and 5. The major improvement in cases 8 and 9

may be attributed to the better separation of the signal envelope features with and without

fault, especially for the combination of signals, compared to that of the original (Table 1)

and filtered signals(Table 3). However, the results for signals 24 are the same (trainingand test success of 100%) with or without any preprocessing. This may be attributed to the

simple processing of signal segments for extraction of features that work well even in the

presence of interfering signals.

6. CONCLUSIONS

An ANN-based procedure is presented for fault diagnosis of rolling element bearings

using features extracted directly from time-domain vibration signal segments through

simple processing. The ANN consists of 45 input nodes, two hidden layers with 16 and 10neurons and two output neurons. The effects of different input signals and features on the

success rate of training and test were studied. The most significant group of vibration

signals and the characteristic features were identified. The accelerometers should be so

placed that the bearing defects have effective contribution to the accelerometer outputs.

Table 5

Effects of signal envelopes on identification of machine condition with five features (rms, s2,

g3, g4, g6)

Case no. Input signals Training success Test success Epochs

1 1 24/24 (100%) 13/16 (81.25%) 282 2 24/24 (100%) 14/16 (87.50%) 173 3 24/24 (100%) 12/16 (75.00%) 334 4 24/24 (100%) 15/16 (93.75%) 245 5 24/24 (100%) 15/16 (93.75%) 196 2,3 48/48 (100%) 32/32 (100%) 127 2,3,4 72/72 (100%) 48/48(100%) 228 1,2,3,4 96/96 (100%) 63/64 (98.44%) 239 1,2,3,4,5 120/120 (100%) 79/80 (98.75%) 32



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The effects of simple preprocessing prior to feature extraction, like high-pass or band-pass

filtration of the vibration signals, with and without demodulation, were studied. The use of

more elaborate signal processing technique like wavelet transform did not improve the

results significantly. The success rate for training was almost 100% and that of test was

quite high 98100%. The training was quite fast requiring significantly small number of

epochs (less than 50). This substantial reduction in training epochs is due to preprocessingof the vibration data and using the substantially small number of extracted features

as inputs to the ANN. The features are effective in ANN-based diagnosis of bearing

failures using both original signals and the high-frequency components of the signals. The

present approach also shows the importance of using multiple signals in the diagnostic

process.

However, the present procedure is used to classify the status of the machine in the form

of normal or faulty bearings. There is a scope for its extension to identify fault types, fault

combinations, and severity levels. The ANN-based approach has its inherent short-

comings that the ANN needs to be trained for each machine condition: normal and

defective with different fault types and severity levels. The data acquisition and ANNtraining may be done at both installation stage and regular operation of the machine.

Another limitation of the ANN-based approach is that the numerical values and the ANN

structure would not probably be optimal for another machine. This may necessitate

further ANN training and may be included in the machine test and condition monitoring

program[13]. Since the ANN training is quite fast requiring substantially small number of

iterations, at least for the cases considered, training and test may be done on-line. These

issues are subjects of further study.

ACKNOWLEDGEMENTS

The dataset was acquired in the Delft Machine diagnostics by neural network project

with help from Landustrie B.V., The Netherlands and can be downloaded freely at the

following web-address:http://www.ph.tn.tudelft.nl/ypma/mechanical.html. The authors

thank Dr. Alexander Ypma of TU Delft for making the dataset available and providing

useful clarifications. The authors would also like to thank the reviewers for their

comments and suggestions that helped in revising the paper to its present form.

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