Fast and robust neural network based wheel bearing fault detection with optimal wavelet features

被引：9
|
作者：
Xu, P [1 ]
Chan, AK [1 ]
机构：
[1] Texas A&M Univ, Dept Elect Engn, College Stn, TX 77843 USA
来源：
PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3 | 2002年
关键词：
D O I：
10.1109/IJCNN.2002.1007461
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We propose a new design of a neural network based system to detect faulty bearings using acoustic signals in a noisy wayside environment. Statistical features are generated from discrete wavelet transform (DWT) coefficients, and a genetic algorithm is used to select the optimal features. The false negative rate for detecting a condemnable bearing is as low as 0.1% regardless of the speed, load condition, and bearing type.
引用
收藏
页码：2076 / 2080
页数：3
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