National Changhua University of Education Institutional Repository : Item 987654321/18461
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题名: Faults Classification of a Scooter Engine Platform Using Wavelet Transform and Artificial Neural Network
作者: Wu, Jian-Da;Chang, En-Chun;Liao, Shu-Yi;Kuo, Jun-Ming;Huang, Cheng-Kai
贡献者: 車輛科技研究所
关键词: Fault diagnosis system;Continuous wavelet transform;Artificial neural network
日期: 2009-03
上传时间: 2014-04-29T07:33:02Z
摘要: 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.
關聯: Proceedings of the International MultiConference of Engineers and Computer Scientists 2009
显示于类别:[車輛科技研究所] 會議論文

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