Decision tree and PCA-based fault diagnosis of rotating machinery
After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, pre...
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| Published in | Mechanical systems and signal processing Vol. 21; no. 3; pp. 1300 - 1317 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Elsevier Ltd
01.04.2007
Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN. |
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| AbstractList | After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN. |
| Author | Li, Jiaqing Chen, Jin Sun, Weixiang |
| Author_xml | – sequence: 1 givenname: Weixiang surname: Sun fullname: Sun, Weixiang email: wxsun@sjtu.edu.cn – sequence: 2 givenname: Jin surname: Chen fullname: Chen, Jin email: jinchen@mail.sjtu.edu.cn – sequence: 3 givenname: Jiaqing surname: Li fullname: Li, Jiaqing email: jqli_vsn@sjtu.edu.cn |
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| Keywords | Fault diagnosis Rotating machinery Data mining Decision tree C4.5 Principal component analysis Data analysis Vibration Rotating machine Fault tree Decision making Unbalanced conditions Data processing Information extraction Pattern recognition Modeling Backpropagation algorithm Shaft Database Signal processing Fault diagnostic Pattern extraction Cracked beam |
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| Snippet | After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on... |
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| SubjectTerms | Applied sciences C4.5 Data mining Decision tree Exact sciences and technology Fault diagnosis Fundamental areas of phenomenology (including applications) Industrial metrology. Testing Measurement and testing methods Mechanical engineering. Machine design Physics Principal component analysis Rotating machinery Solid mechanics Structural and continuum mechanics Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...) |
| Title | Decision tree and PCA-based fault diagnosis of rotating machinery |
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