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Fault diagnosis of a mine hoist using PCA and SVM techniques |
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| Authors: | CHANG Yan-wei WANG Yao-cai LIU Tao WANG Zhi-jie |
| Affiliation: | 1. School of lnformation and Electrical Engineering, China University of Mining & Technology, Xuzhou, Jiangsu 221008, China;School of Mechanical and Electrical Engineering, Xuzhou Normal University, Xuzhou, Jiangsu 221116, China 2. School of Mechanical and Electrical Engineering, Xuzhou Normal University, Xuzhou, Jiangsu 221116, China 3. Department of Electronics & Information Technology, Suzhou Vocational University, Suzhou, Jiangsu 215104, China 4. School of lnformation and Electrical Engineering, China University of Mining & Technology, Xuzhou, Jiangsu 221008, China |
| Abstract: | A new method based on principal component analysis (PCA) and support vector machines (SVMs) is proposed for fault diagnosis of mine hoists. PCA is used to extract the principal features associated with the gearbox. Then, with the irrelevant gearbox variables removed, the remaining gearbox, the hydraulic system and the wire rope parameters were used as input to a multi-class SVM. The SVM is first trained by using the one class-based multi-class optimization algorithm and it is then applied to fault identification. Comparison of various methods showed the PCA-SVM method successfully removed redundancy to solve the dimensionality curse. These results show that the algorithm using the RBF kernel function for the SVM had the best classification properties. |
| Keywords: | fault diagnosis principal component analysis support vector machines mine hoist |
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