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Rolling Bearing Fault Diagnosis Method based on EEMD and GBDBN

Volume 15, Number 1, January 2019, pp. 230-240
DOI: 10.23940/ijpe.19.01.p23.230240

Zhiwu Shang, Xia Liu, Xiangxiang Liao, Rui Geng, Maosheng Gao, and Jintian Yun

Tianjin Key Laboratory of Modern Mechatronics Equipment Technology, Tianjin Polytechnic University, Tianjin, 300387, China

(Submitted on October 13, 2018; Revised on November 14, 2018; Accepted on December 17, 2018)


Aiming at the complexity, nonlinearity, and non-stationarity of the rolling bearing vibration signal, a fault diagnosis method based on Ensemble Empirical Mode Decomposition (EEMD) and Gauss Bernoulli Deep Belief Network (GBDBN) model is proposed. The method first carries out EEMD on the vibration signal; second, the eigenvalues of each intrinsic mode function (IMF) are statistically analyzed; then, the feature vectors are constructed by selecting less change features; finally, the normalized feature vectors are input into the GBDBN to identify the fault types. The experimental results show that the proposed method achieves more than 90% recognition rate of fault types and has better fault diagnosis ability, which can provide convenience for maintenance.


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