Bearing Fault Pattern Recognition Using Harmonic Wavelet Sample Entropy and Hidden Markov Model

Abstract

Abstract: Abstract： According to the excellent characteristics of the nonstationary vibration signals decompose with harmonic wavelet and the strong temporal pattern classification ability of hidden Markov model(HMM), a bearing fault pattern recognition based on harmonic wavelet sample entropy and HMM was proposed. First, by applying the harmonic wavelet to decompose each bearing fault signal, the frequency layer characteristics of harmonic wavelet threedimensional time frequency trellis was used to estimate the reasonable sample entropy dimension and threshold value, and the feature vector sequence was constructed by extracting sample entropy of rolling bearing each layer vibration signal. Then, the feature vector sequence of former 120 groups were input into HMM to be trained, so as to acquire various models corresponding to different faults，and the remaining 80 groups are tested and identified. Finally, the bearing fault types were identified by comparing the logarithmic likelihood probability value. The actual identify results and comparison with the BP, RBF neural network model prove that this coupling method has better identification accuracy and stability.

Key words: Key words： harmonic wavelet, sample entropy, hidden Markov model(HMM), rolling bearing, pattern recognition

CLC Number:

LI Qing1,LIANG Steven Y1,2,YANG Jianguo1. Bearing Fault Pattern Recognition Using Harmonic Wavelet Sample Entropy and Hidden Markov Model[J]. Journal of Shanghai Jiaotong University, 2016, 50(05): 723-729.

References

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