Pattern recognition for composite fault based on wavelet neural networks

被引：0

作者：

Hu, Shou-Song ^{[1
]}

Zhou, Chuan ^{[1
]}

Wang, Yuan ^{[1
]}

机构：

[1] Dept. of Automat. Control, Nanjing Univ. of Aero. and Astron., Nanjing 210016, China

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2002年 / 28卷 / 04期

关键词：

Complex nonlinear systems - Composite faults - Fault character vectors - Fault diagnosis - Wavelet neural networks;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

A pattern recognition method for composite fault diagnosis based on wavelet neural networks is presented. Considering the multiple faults in complex nonlinear system such as a fighter, a multiple-layer wavelet neural network is constructed. Residual signal is processed by discrete binary wavelet transformation at the input layer, and the detail coefficients are obtained under multi-resolution as fault character vectors, finally these character vectors are sent to the neural network classifier to complete fault pattern recognition. Simulation results reveal that the presented method is effective for composite fault diagnosis.

引用

页码：540 / 543

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