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Discrimination the Roller Bearing Faults with Harmonic Wavelet Package, Kernel Principal Component Analysis and Relevance Vector Machine

For the purpose of discriminating the roller bearing faults, this paper proposes a novel method with harmonic wavelet package, Kernel Principal Component Analysis (KPCA) and Relevance Vector Machine (RVM). Firstly, compute the vector energy with wavelet coefficients after the roller bearing vibration signals have been decomposed with harmonic wavelet package. The feature vectors are available after the vector energy has been standardized. Secondly, this paper uses several kernel principal components to extract nonlinear feature from the former vector according to the cumulative principle component contribution rate while greater than 90 %. Thirdly, a multiple classification model is proposed with RVM to discriminate the roller bearing faults. Finally, vibration data from Case Western Reserve University is used to test the proposed method. The experimental results show the proposed method reflects more roller bearing fault characteristics than the conventional method. Meanwhile, it can identify roller bearing states more accurately and efficiently. Copyright © 2014 IFSA Publishing, S. L.