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NORPIE 2004 Trondheim, 14 June. Automatic bearing fault classification combining statistical classification and fuzzy logic Tuomo Lindh Jero Ahola Petr Spatenka Anna-Lena Rautiainen www.lut.fi. Introduction to fault classification. - PowerPoint PPT Presentation
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NORPIE 2004Trondheim, 14 June
Automatic bearing fault classification combining statistical classification and fuzzy logic
Tuomo Lindh
Jero Ahola
Petr Spatenka
Anna-Lena Rautiainen
www.lut.fi
Rotor10 %
Stator37 %Bearing
41 %
Others12 %
Introduction to fault classification
Neural networks, unsupervised Kohonen’s maps, supervised BPStatistical classification, Mahalanobis distance, Support vector MachinesHidden Markov chains,Fuzzy logicAdaptive, model based methods and many others
Deterministic Stochastic, probabilisticknown parameters Pattern classificationparameter estimation neural networksrule based inference statistical methodsfuzzy logicMathematical modelling
Why probabilistic and deterministic ?
• Statistical pattern classification
• search patterns of features
• compare with known prototypes of faults
• Deterministic calculation, rules
• calculates the magnitudes
• estimates the risk of fault classification
• calculates the quality of features• avoid known pit falls (classification method, behaviour of machines etc.)
Appearance of cyclic bearing faults in motor frame vibration
Time
Fre
quen
cy
0 0.02 0.04 0.06 0.08 0.1 0.120
2000
4000
6000
8000
10000
12000
14000
16000
Time
Fre
quen
cy
0 0.02 0.04 0.06 0.08 0.1 0.120
2000
4000
6000
8000
10000
12000
14000
16000
)()(*)()()()()( ,, tnthtxtxtxtxtyj
jnmi
imtpbeme
Wang (1998), Lindh (2002)
0 2 4 6 8 10 12 14 160
1
2
3
4
5
6
7
8
9
features
pro
toty
pe
ve
cto
r v
alu
e
0 100 200 300 400 500 6000
1
2
3
4
5
6x 10
-3
f [Hz]
0 2 4 6 8 10 12 14 160
2
4
6
8x 10
-3
Outerrace
Innerrace
rollingelement
cage
testvector
features
Statistical classification
n
lll mxmx
nc
1kk,ii,ik 1
1
yx1
xyyx2 mm'mm Cr
Minimum distance classifier
0 100 200 300 400 500 600
0
1
2
3
4
5
6x 10
-3
f [Hz]
expected bearing pass frequencies *
rotational side band frequencies o
other maximum value x
Fuzzy logic, the fault size estimation
0 100 200 300 400 500 6000
1
2
3
4
5
6x 10
-3
f [Hz]
expected bearing pass frequencies *
rotational side band frequencies o
other maximum value x
Fuzzy logic, the probability of the fault
uses the results of the
statistical classification as input
Fault classifier
Feature extractionEnvelope spectrumformation
Signal Statistical distancecalculation
Minimum distanceclassifier
Feature extraction
Fuzzy logics
Classificationdata
Result
RMS, peaks, etc.
Results
0 100 200 300 400 500 6000
1
2
3
4
5
6x 10
-3
f [Hz]
0 2 4 6 8 10 12 14 160
1
2
3
4
5
6x 10
-3
Outerrace
Innerrace
rollingelement cage
testvector
features
0 100 200 300 400 500 6000
1
2
3
4
5
6x 10
-3
f [Hz]
0 2 4 6 8 10 12 14 160
1
2
3
4x 10
-3
Outerrace
Innerrace
rollingelement
cage
testvector
features
Results
Classification results using 16- dimensional feature space covering all four fault types.
The bold cases are selected with the minimum distance classifier.
healthy outer race inner race ball spin cagehealthy 0.6981 1.3607 2.7905 1.6052 2.3892outer race 5.3817 1.2018 7.0394 8.7226 7.5465inner race 2.9616 3.2331 1.0653 2.7675 2.7684ball spin 6.5451 5.3318 8.2085 1.0579 6.7352cage 0.7758 1.4425 0.8656 1.7417 0.1666
Results
Classification results using four dimensional feature space. The distance between test vector and any fault prototypes are calculated separately. The bold cases are selected with the minimum distance classifier.
test data distance to outer race inner race ball spin cagehealthy healthy 0.1767 0.148 0.1107 0.138
broken 0.781 4.235 1.2339 1.3203outer race healthy 3.9542 0.2946 0.2454 0.2164
broken 0.0394 6.5133 1.4492 1.4779inner race healthy 0.556 2.4608 0.441 2.0332
broken 1.385 0.3182 1.6345 2.656ball spin healthy 0.2327 0.3109 4.4202 0.1176
broken 0.9328 4.1784 0.2219 1.7265cage healthy 0.2827 0.3915 0.2275 4.0238
broken 1.0563 5.0148 1.7709 0.073
Results
Fault degreetest data outer race inner race ball spin cage
healthy 0.18 0.17 0.17 0.18outer race 0.47 0.42 0.18 0.10inner race 0.03 0.36 0.03 0.32ball spin 0.18 0.17 0.50 0.18cage 0.18 0.16 0.17 0.50
Classification results using simple fuzzy logic for the determination of fault degree
Results
Classification results using fuzzy logic that estimates the fault degree as well as the probability of the faults. Action can be formed with fuzzy logic or multiplication of the fields of table of previous slide with the probabilities of this table. The actions are selected with trigger levels.
test data outer race inner race ball spin cage outer race inner race ball spin cagehealthy 0.18 0.17 0.16 0.17 0.03 0.03 0.03 0.03outer race 0.49 0.3 0.3 0.2 0.23 0.13 0.05 0.02inner race 0.32 0.35 0.17 0.16 0.01 0.13 0.01 0.05ball spin 0.32 0.17 0.17 0.16 0.06 0.03 0.09 0.03cage 0.32 0.17 0.16 0.58 0.06 0.03 0.03 0.29
Fault probability Action
Conclusions
The method where the Mahalanobis distance based statistical classification results were used as one input in fuzzy logic was introduced.
This input and other qualitative features of the spectrum were taken into account in the fuzzy logic which evaluated the probability of a certain fault. The other fuzzy logic evaluated the degree of certain fault and by combining the outputs of these logics, the final suggestion of the state and required action were given.
What?
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