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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hsuan-Wen Peng Pei-Ju Chiang |
| Copyright Year | 2011 |
| Description | Author affiliation: Dept. of Mechanical Engineering, National Chung Cheng University, Taiwan (Hsuan-Wen Peng; Pei-Ju Chiang) |
| Abstract | Ball bearing fault is one of the main causes of induction motor failure. This paper investigates in the fault diagnosis of ball bearing of three phase induction motor using random forest algorithm and C4.5 decision tree. The bearing conditions are classified to four categories: normal, bearing with inner race fault, bearing with ball fault and bearing with outer race fault. The statistical features used for classification are extracted from mechanical vibration signal in time domain and frequency domain. Principal component analysis (PCA) and linear discriminent analysis (LDA) are used to reduce the dimension and complexity of the feature set. The classification accuracy of random forest algorithm and C4.5 decision tree are analyzed and compared. The experimental results show that the random forest algorithm not only works better than the C4.5 decision tree but also can classify the ball bearing condition effectively. |
| Starting Page | 175 |
| Ending Page | 180 |
| File Size | 248882 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781612844879 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-05-15 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Asian Control Association |
| Subject Keyword | Training Accuracy Induction motors Random forest algorithm C4.5 decision tree Feature extraction Ball bearing fault diagnosis Principal component anslysis Classification algorithms Decision trees Linear discriminent analysis Principal component analysis |
| Content Type | Text |
| Resource Type | Article |
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