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- Title
Rolling Bearing Fault Diagnosis Method based on EEMD and GBDBN.
- Authors
Zhiwu Shang; Xia Liu; Xiangxiang Liao; Rui Geng; Maosheng Gao; 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.
- Subjects
BOLTZMANN machine; FAILURE analysis; BEARINGS (Machinery) -- Vibration; EIGENVALUES; HILBERT-Huang transform
- Publication
International Journal of Performability Engineering, 2019, Vol 15, Issue 1, p230
- ISSN
0973-1318
- Publication type
Article
- DOI
10.23940/ijpe.19.01.p23.230240