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

Abstract:

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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