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- Title
A rolling bearing fault diagnosis method based on fastDTW and an AGBDBN.
- Authors
Shang Zhiwu; Liu Xia; Li Wanxiang; Gao Maosheng; Yu Yan
- Abstract
In order to improve fault feature extraction and diagnosis for rolling bearings, a fault diagnosis method based on fast dynamic time warping (fastDTW) and an adaptive Gaussian-Bernoulli deep belief network (AGBDBN) is proposed in this paper. Firstly, for the non-stationary vibration signal characteristics of the bearing, the fastDTW algorithm is used to calculate the residual vector of the fault signal, thereby enhancing the fault characteristic information. Then, according to the continuous vibration value of the bearing vibration signal, a standard deep belief network (DBN) is improved to deal with the problem that the optimal setting for the learning rate is difficult to achieve in the deep neural network training process and the AGBDBN model is used for fault diagnosis. Finally, the proposed method is compared with a variety of model diagnosis methods. The experimental results show that the proposed method achieved good diagnostic results.
- Subjects
FAULT diagnosis; DIAGNOSIS methods; ROLLER bearings; FEATURE extraction; REINFORCEMENT learning; ALGORITHMS; RATE setting
- Publication
Insight: Non-Destructive Testing & Condition Monitoring, 2020, Vol 62, Issue 8, p457
- ISSN
1354-2575
- Publication type
Article
- DOI
10.1784/insi.2020.62.8.457