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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.
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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.
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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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Sequence no: 1_72 training data 73_120 test
Targets/netoutputs
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Sequence no: 1_72 training data 73_120 test data
Targets/netoutputs
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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