DocumentCode
1677304
Title
Fast and robust neural network based wheel bearing fault detection with optimal wavelet features
Author
Xu, Peng ; Chan, Andrew K.
Author_Institution
Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA
Volume
3
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
2076
Lastpage
2080
Abstract
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 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
Keywords
condition monitoring; discrete wavelet transforms; fault diagnosis; genetic algorithms; machine bearings; neural nets; railways; condition monitoring; discrete wavelet transform; fault detection; genetic algorithm; neural network; optimal features selection; railways; wheel bearings; Acoustic noise; Acoustic signal detection; Discrete wavelet transforms; Fault detection; Genetic algorithms; Neural networks; Robustness; Signal design; Wheels; Working environment noise;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1007461
Filename
1007461
Link To Document