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請使用永久網址來引用或連結此文件:
http://ir.ncue.edu.tw/ir/handle/987654321/18363
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| 題名: | An Expert System for Fault Diagnosis in Internal Combustion Engines Using Wavelet Packet Transform and Neural Network |
| 作者: | Wu, Jian-Da;Liu, Chiu-Hong |
| 貢獻者: | 車輛科技研究所 |
| 關鍵詞: | Wavelet packet transform;Neural network;Fault diagnosis;Sound emission signal |
| 日期: | 2009-04
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| 上傳時間: | 2014-04-29T07:28:14Z
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| 出版者: | Elsevier Ltd |
| 摘要: | In the present study, a fault diagnosis system is proposed for internal combustion engines using wavelet packet transform (WPT) and artificial neural network (ANN) techniques. In fault diagnosis for mechanical systems, WPT is a well-known signal processing technique for fault detection and identification. The signal processing algorithm of the present system is gained from previous work used for speech recognition. In the preprocessing of sound emission signals, WPT coefficients are used for evaluating their entropy and treated as the features to distinguish the fault conditions. Obviously, WPT can improve the continuous wavelet transform (CWT) used over a longer computing time and huge operand. It can also solve the frequency-band disagreement by discrete wavelet transform (DWT) only breaking up the approximation version. In the experimental work, the wavelets are used as mother wavelets to build and perform the proposed WPT technique. In the classification, to verify the effect of the proposed generalized regression neural network (GRNN) in fault diagnosis, a conventional back-propagation network (BPN) is compared with a GRNN network. The experimental results showed the proposed system achieved an average classification accuracy of over 95% for various engine working conditions. |
| 關聯: | Expert Systems with Applications, 36(3)Part1: 4278-4286 |
| 顯示於類別: | [車輛科技研究所] 期刊論文
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