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Automatic Classification of Gear-Units by Neural Network Technologies
Copyright © IFAC Artificial Intelligence in Real-Time Control , Arizona, USA, 1998
AUTOMATIC CLASSIFICATION OF GEAR-UNITS BY NEURAL NETWORK TECHNOLOGIES
Ingomar Wascher Volkmar H. Haase
University o/Technology, Graz, Austria lICM-Software Technology Muenzgrabenstrasse II A-80JO, Graz
Abstract: Noise emitted during test runs of mitre-gear units was analyzed using a neural network based tool. Gears can be classified dependent on how noise levels vary with changes of revolution speed. Faults can be detected and necessary adjustments can be identified. The kernel of the work is the appropriate selection, reduction and preprocessing of input data, parameterization of the clustering (unsupervised learning) algorithms, and the building of the classification model (supervised learning). Copyright © 1998 lFAC
Keywords: Automobile industry, Data processing, Data reduction, Mechanical systems, Model-based control, Neural networks, Noise analysis, Statistics
The noise level can therefore be expressed with the following "formula":
I. THE PRACTICAL PROBLEM This paper contains the main results of a diploma thesis done for Zurk GmbH, see (Zurk, 1998), at IICM - Software Technology at University of Technology in Graz, Austria.
Noise level = f(number of revolutions/sec., possible defects, necessary adjustments) Figures I and 2 show the noise level and the corresponding number of revolutions of a typical test run .
The purpose of the thesis is to support the product testing of mitre-gear units for large trucks to shorten test cycles and to avoid disassembly of gear-units after testing.
Based on measurements of approximately 150 gear units neural networks were used to cluster the measurement records to find groups of gear units behaving in a similar way.
Mitre-gear units are immediately after the production tested in test-beds where the number of revolutions is varied during a 948 sec. test run simulating real road situations.
If types of gear units can be identified in this manner any new test run of a gear unit will deliver a result e.g. gear unit is of type I . Now engineers know from experience that this means e.g. tighten ... (without disassembling the whole gear unit, as it has been necessary before).
Test runs deliver a set of parameters (noise, temperature, oil pressure, etc.) each second. Noise is most promising to classify types of gears. It is measured to get information about possible faults or necessary adjustments.
275
In the next step the incomplete sets of data have been eliminated which led to 151 remaining records. Afterwards these records including 948 time units were shortened to 589 time units each by cutting off the early (time < 171) and late phases (time> 760). They were omitted because of external disturbances.
40 · 35 -
30-
~
25 -
Cl
.
-; 20-
g 15 ; 10
Also powerful computers (300 MHz Pentium) and neural network tools are unable to process some 500 parameters in reasonable time. The number of test points used from the test run was therefore reduced to 8 covering typical time-spans (where the revolution speed is constant for some 50 seconds) within the run. These main levels, exactly speaking the mean values of the levels, have been taken for further analysis. Figure 3 shows these 8 points .
5 -
0-·-------------------------------n1 171 221 271 32 1 371 421
471 521 571 621 671 721
time units (5)
Fig 1. Noise-level of a typical shortened test run 2500 . Cl)
c 2000 -
.E
~
1500 -
~ .: 1000
o
. 4
4. DATA ANAL YZING AND CLUSTERING
I
:; 500 : E g o-----------;~----~--~~--~~
.Q
171 221 271 321 371 421 471 521 571 621 671 721 -500 -
The method used for data analyzing was unsupervised learning, see (pao, 1989). Business Adviser supports this method in the form of clustering by similarities and offers the possibility to vary the following parameters: Maximum Cluster Levels, Desired Maximum Clusters Per Level, Cluster Radius Change Rate (%), Desired Maximum ofT' Population Clusters (%).
n1
time units (5)
Fig 2. Corresponding number of revolutions
2. THE DA TA ANALYSIS TOOL
In the experiment these parameters have been varied to fmd out which clustering results were more or less stable over various parameter settings.
The tool used for this work is Business Advisor from AIW ARE, now acquired by Computer Associates. Business Advisor runs under Win95INT and is a complete modeling and decision support system.
In this step similarities between the different clustering results obtained were identified. This was done by comparing the several results and computing the intersecting clusters using a self made c++ program.
It is based on neural networks and can be used either for unsupervised learning, Le. learning by similarities, or for supervised learning. Furthermore it offers simple to use statistical functionality .
As known unsupervised learning is very sensible to outliers. Therefore it was necessary to eliminate them which was easily done by removing the very small clusters.
3. PREPROCESSING OF DATA From several thousand sets of test data (different types of gears), each containing some 900 records with 8 parameters a set of approximately 200 test runs (one type of gear) was chosen.
The remaining smaller set of data was clustered once again with different values of the parameters leading to an iterative process.
190 240
The iteration has been carried out until the resulting clusters were compact and stable and no more outliers could be identified, i.e. no data item did change between two clusters .
35 30 -
. Cl
'0
25 -
20 -
c::
15 -
The unsupervised learning algorithm finally resulted in 104 data items split up into 8 different clusters as can be seen in figure 4. The size of the clusters varies from 7 items to 27 items each as shown in table 1. There are 3 clusters with a size smaller than 10. Omitting them as done in Fig 5 is increasing the readability.
10
5 -
0 - - - - - -___________________________ 171
221
271
321 371 421 471 521 571 621 671 721
n1
time units (5)
Fig 3. Final eight mean values used for the further process
276
Table 2 Typical result of the classification
~~--------------------~--------. 40
Data Item record 19 record 42 record 64 record 66 record 67 record 103 record 110 record 122 record 126 record 139
30 20 CD
-ac
10
-1 0 -20 -30 L -__
~~:!....!..-
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _----,
time units (s)
Fig 4. Mean values of the all eight resulting clusters
30 20 CD
c
Predicted Class 0 .999429 1.86426 6.88321 -0.0443008 -0.044644 2.75161 3.82175 0.119704 4 .59796 5.9722
Error ok ok ok ok ok ok ok not ok (ok) ok
Using the preceding results a model has been build in order to be able to classify new data items, see (pao, 1989). Again Business Advisor was used for this purpose with the following parameters: Two hidden layers with 5 and 3 nodes, Gaussian RBF nodes as functional link option, system error of 0.0001 , independent outputs, node outputs use sigmoidal slope of 0.1 and the learning algorithm was accelerated backpropagation.
40
~
Class 1 2 7 0 0 3 4 1 5 6
10
-20
The training set R2-value ranges from 98 to 99.8 depending on the selected set of testing data and the test set R2-value ranges from 80 to 98.
-30
time units (s)
Fig 5. Mean values of the five main clusters for better readability
A typical result is shown in table 2. There the real cluster nwnber (Class) of the gear units (Data Item), the classification by the learned model (Predicted Class) and the error done by the classification can be seen. With the exception of record 122 the error value is satisfying.
Table I The resulting 8 clusters and their size (nwnber of members) Cluster Size
o 18
13
2 12
3 8
4 27
5 7
6
7
12
7
The result shows that there are 3 clusters (I, 2 and 6) which show the same time dependent behavior during the test run. The difference between them is that noise levels in cluster 6 are higher than in cluster 2 which are higher than in cluster 1.
CONCLUSION These experiments demonstrate that analysis of noise data during test runs of mitre-gear units is a powerful instrwnent to identify faults and necessary adjustments. Classification of gear units informs the mechanical engineers of repair measures without being obliged to disassemble each gear unit after the test.
But if cluster 0 is regarded one can see that it starts with low values and approaches high values at the end of the run . Obviously the behavior of these gear units changes with time - adjustments seem necessary.
REFERENCES 5. BUILDING A MODEL
Pao, Y .H. (1989). Adaptive pattern recognition and neural networks. Addison-Wesley, Reading. Zurk, A.P . (1998). Private Communication. Graz.
The aim of the next experiment was to build a model for mitre-gear units with supervised learning to be able to predict the type (i.e. the cluster nwnber) for any new gear unit, that has gone through the testing process in the test beds. This would inform the mechanical engineers about possible defects or help them adjusting the gear parameters.
277
AUTOMATIC CLASSIFICATION OF GEAR-UNITS BY NEURAL NETWORK TECHNOLOGIES
Ingomar Wascher Volkmar H. Haase
University o/Technology, Graz, Austria lICM-Software Technology Muenzgrabenstrasse II A-80JO, Graz
Abstract: Noise emitted during test runs of mitre-gear units was analyzed using a neural network based tool. Gears can be classified dependent on how noise levels vary with changes of revolution speed. Faults can be detected and necessary adjustments can be identified. The kernel of the work is the appropriate selection, reduction and preprocessing of input data, parameterization of the clustering (unsupervised learning) algorithms, and the building of the classification model (supervised learning). Copyright © 1998 lFAC
Keywords: Automobile industry, Data processing, Data reduction, Mechanical systems, Model-based control, Neural networks, Noise analysis, Statistics
The noise level can therefore be expressed with the following "formula":
I. THE PRACTICAL PROBLEM This paper contains the main results of a diploma thesis done for Zurk GmbH, see (Zurk, 1998), at IICM - Software Technology at University of Technology in Graz, Austria.
Noise level = f(number of revolutions/sec., possible defects, necessary adjustments) Figures I and 2 show the noise level and the corresponding number of revolutions of a typical test run .
The purpose of the thesis is to support the product testing of mitre-gear units for large trucks to shorten test cycles and to avoid disassembly of gear-units after testing.
Based on measurements of approximately 150 gear units neural networks were used to cluster the measurement records to find groups of gear units behaving in a similar way.
Mitre-gear units are immediately after the production tested in test-beds where the number of revolutions is varied during a 948 sec. test run simulating real road situations.
If types of gear units can be identified in this manner any new test run of a gear unit will deliver a result e.g. gear unit is of type I . Now engineers know from experience that this means e.g. tighten ... (without disassembling the whole gear unit, as it has been necessary before).
Test runs deliver a set of parameters (noise, temperature, oil pressure, etc.) each second. Noise is most promising to classify types of gears. It is measured to get information about possible faults or necessary adjustments.
275
In the next step the incomplete sets of data have been eliminated which led to 151 remaining records. Afterwards these records including 948 time units were shortened to 589 time units each by cutting off the early (time < 171) and late phases (time> 760). They were omitted because of external disturbances.
40 · 35 -
30-
~
25 -
Cl
.
-; 20-
g 15 ; 10
Also powerful computers (300 MHz Pentium) and neural network tools are unable to process some 500 parameters in reasonable time. The number of test points used from the test run was therefore reduced to 8 covering typical time-spans (where the revolution speed is constant for some 50 seconds) within the run. These main levels, exactly speaking the mean values of the levels, have been taken for further analysis. Figure 3 shows these 8 points .
5 -
0-·-------------------------------n1 171 221 271 32 1 371 421
471 521 571 621 671 721
time units (5)
Fig 1. Noise-level of a typical shortened test run 2500 . Cl)
c 2000 -
.E
~
1500 -
~ .: 1000
o
. 4
4. DATA ANAL YZING AND CLUSTERING
I
:; 500 : E g o-----------;~----~--~~--~~
.Q
171 221 271 321 371 421 471 521 571 621 671 721 -500 -
The method used for data analyzing was unsupervised learning, see (pao, 1989). Business Adviser supports this method in the form of clustering by similarities and offers the possibility to vary the following parameters: Maximum Cluster Levels, Desired Maximum Clusters Per Level, Cluster Radius Change Rate (%), Desired Maximum ofT' Population Clusters (%).
n1
time units (5)
Fig 2. Corresponding number of revolutions
2. THE DA TA ANALYSIS TOOL
In the experiment these parameters have been varied to fmd out which clustering results were more or less stable over various parameter settings.
The tool used for this work is Business Advisor from AIW ARE, now acquired by Computer Associates. Business Advisor runs under Win95INT and is a complete modeling and decision support system.
In this step similarities between the different clustering results obtained were identified. This was done by comparing the several results and computing the intersecting clusters using a self made c++ program.
It is based on neural networks and can be used either for unsupervised learning, Le. learning by similarities, or for supervised learning. Furthermore it offers simple to use statistical functionality .
As known unsupervised learning is very sensible to outliers. Therefore it was necessary to eliminate them which was easily done by removing the very small clusters.
3. PREPROCESSING OF DATA From several thousand sets of test data (different types of gears), each containing some 900 records with 8 parameters a set of approximately 200 test runs (one type of gear) was chosen.
The remaining smaller set of data was clustered once again with different values of the parameters leading to an iterative process.
190 240
The iteration has been carried out until the resulting clusters were compact and stable and no more outliers could be identified, i.e. no data item did change between two clusters .
35 30 -
. Cl
'0
25 -
20 -
c::
15 -
The unsupervised learning algorithm finally resulted in 104 data items split up into 8 different clusters as can be seen in figure 4. The size of the clusters varies from 7 items to 27 items each as shown in table 1. There are 3 clusters with a size smaller than 10. Omitting them as done in Fig 5 is increasing the readability.
10
5 -
0 - - - - - -___________________________ 171
221
271
321 371 421 471 521 571 621 671 721
n1
time units (5)
Fig 3. Final eight mean values used for the further process
276
Table 2 Typical result of the classification
~~--------------------~--------. 40
Data Item record 19 record 42 record 64 record 66 record 67 record 103 record 110 record 122 record 126 record 139
30 20 CD
-ac
10
-1 0 -20 -30 L -__
~~:!....!..-
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _----,
time units (s)
Fig 4. Mean values of the all eight resulting clusters
30 20 CD
c
Predicted Class 0 .999429 1.86426 6.88321 -0.0443008 -0.044644 2.75161 3.82175 0.119704 4 .59796 5.9722
Error ok ok ok ok ok ok ok not ok (ok) ok
Using the preceding results a model has been build in order to be able to classify new data items, see (pao, 1989). Again Business Advisor was used for this purpose with the following parameters: Two hidden layers with 5 and 3 nodes, Gaussian RBF nodes as functional link option, system error of 0.0001 , independent outputs, node outputs use sigmoidal slope of 0.1 and the learning algorithm was accelerated backpropagation.
40
~
Class 1 2 7 0 0 3 4 1 5 6
10
-20
The training set R2-value ranges from 98 to 99.8 depending on the selected set of testing data and the test set R2-value ranges from 80 to 98.
-30
time units (s)
Fig 5. Mean values of the five main clusters for better readability
A typical result is shown in table 2. There the real cluster nwnber (Class) of the gear units (Data Item), the classification by the learned model (Predicted Class) and the error done by the classification can be seen. With the exception of record 122 the error value is satisfying.
Table I The resulting 8 clusters and their size (nwnber of members) Cluster Size
o 18
13
2 12
3 8
4 27
5 7
6
7
12
7
The result shows that there are 3 clusters (I, 2 and 6) which show the same time dependent behavior during the test run. The difference between them is that noise levels in cluster 6 are higher than in cluster 2 which are higher than in cluster 1.
CONCLUSION These experiments demonstrate that analysis of noise data during test runs of mitre-gear units is a powerful instrwnent to identify faults and necessary adjustments. Classification of gear units informs the mechanical engineers of repair measures without being obliged to disassemble each gear unit after the test.
But if cluster 0 is regarded one can see that it starts with low values and approaches high values at the end of the run . Obviously the behavior of these gear units changes with time - adjustments seem necessary.
REFERENCES 5. BUILDING A MODEL
Pao, Y .H. (1989). Adaptive pattern recognition and neural networks. Addison-Wesley, Reading. Zurk, A.P . (1998). Private Communication. Graz.
The aim of the next experiment was to build a model for mitre-gear units with supervised learning to be able to predict the type (i.e. the cluster nwnber) for any new gear unit, that has gone through the testing process in the test beds. This would inform the mechanical engineers about possible defects or help them adjusting the gear parameters.
277