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NCUEIR > College of Technology > vr > Periodical Articles > Item 987654321/18363

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/18363

Title:	An Expert System for Fault Diagnosis in Internal Combustion Engines Using Wavelet Packet Transform and Neural Network
Authors:	Wu, Jian-Da;Liu, Chiu-Hong
Contributors:	車輛科技研究所
Keywords:	Wavelet packet transform;Neural network;Fault diagnosis;Sound emission signal
Date:	2009-04
Issue Date:	2014-04-29T07:28:14Z
Publisher:	Elsevier Ltd
Abstract:	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.
Relation:	Expert Systems with Applications, 36(3)Part1: 4278-4286
Appears in Collections:	[vr] Periodical Articles

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