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Please use this identifier to cite or link to this item:
http://ir.ncue.edu.tw/ir/handle/987654321/18461
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| Title: | Faults Classification of a Scooter Engine Platform Using Wavelet Transform and Artificial Neural Network |
| Authors: | Wu, Jian-Da;Chang, En-Chun;Liao, Shu-Yi;Kuo, Jun-Ming;Huang, Cheng-Kai |
| Contributors: | 車輛科技研究所 |
| Keywords: | Fault diagnosis system;Continuous wavelet transform;Artificial neural network |
| Date: | 2009-03
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| Issue Date: | 2014-04-29T07:33:02Z
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| Abstract: | This paper describes the development of a mechanical fault diagnosis system for a scooter engine platform using continuous wavelet transform and artificial neural network techniques. Most of the conventional techniques for fault diagnosis in a mechanical system are based primarily on analyzing the difference of signal amplitude in the time domain or frequency spectrum. In the present study, a continuous wavelet transform (CWT) algorithm combined with a feature selection method is proposed for analyzing fault signals in a scooter fault diagnosis system. The artificial neural network technique using back-propagation and generalized regression are both used in the proposed system. The effectiveness of the proposed system using two algorithms in CWT technique for scooter fault diagnosis are investigated and compared. The experimental results indicated that the proposed system achieved a fault recognition rate over 95% in the experimental platform of scooter fault diagnosis system. |
| Relation: | Proceedings of the International MultiConference of Engineers and Computer Scientists 2009 |
| Appears in Collections: | [車輛科技研究所] 會議論文
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