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题名: Investigation of Engine Fault Diagnosis Using Discrete Wavelet Transform and Neural Network
作者: Wu, Jian-Da;Liu, Chiu-Hong
贡献者: 車輛科技研究所
关键词: Discrete wavelet transform;Neural network;Fault diagnosis;Sound emission signal
日期: 2008-10
上传时间: 2014-04-29T07:28:07Z
出版者: Elsevier Ltd.
摘要: An investigation of a fault diagnostic technique for internal combustion engines using discrete wavelet transform (DWT) and neural network is presented in this paper. Generally, sound emission signal serves as a promising alternative to the condition monitoring and fault diagnosis in rotating machinery when the vibration signal is not available. Most of the conventional fault diagnosis techniques using sound emission and vibration signals are based on analyzing the signal amplitude in the time or frequency domain. Meanwhile, the continuous wavelet transform (CWT) technique was developed for obtaining both time-domain and frequency-domain information. Unfortunately, the CWT technique is often operated over a longer computing time. In the present study, a DWT technique which is combined with a feature selection of energy spectrum and fault classification using neural network for analyzing fault signal is proposed for improving the shortcomings without losing its original property. The features of the sound emission signal at different resolution levels are extracted by multi-resolution analysis and Parseval's theorem [Gaing, Z. L. (2004). Wavelet-based neural network for power disturbance recognition and classification. IEEE Transactions on Power Delivery 19, 1560-1568]. The algorithm is obtained from previous work by Daubechies [Daubechies, I. (1988). Orthonormal bases of compactly supported wavelets. Communication on Pure and Applied Mathematics 41, 909-996.], the''db4'', ''db8'' and ''db20'' wavelet functions are adopted to perform the proposed DWT technique. Then, these features are used for fault recognition using a neural network. The experimental results indicated that the proposed system using the sound emission signal is effective and can be used for fault diagnosis of various engine operating conditions.
關聯: Expert Systems with Applications, 35(3): 1200-1213
显示于类别:[車輛科技研究所] 期刊論文


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