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

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2 Author(s)
Peng Xu ; Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA ; Chan, A.K.

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

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Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on  (Volume:3 )

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