Rolling bearing fault diagnosis based on SSA optimized self-adaptive DBN

Due to the structure of rolling bearings and the complexity of the operating environment, collected vibration signals tend to show strong non-stationary and time-varying characteristics. Extracting useful fault feature information from actual bearing vibration signals and identifying bearing faults...

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Veröffentlicht in:ISA transactions Jg. 128; H. Pt B; S. 485 - 502
Hauptverfasser: Gao, Shuzhi, Xu, Lintao, Zhang, Yimin, Pei, Zhiming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Ltd 01.09.2022
Schlagworte:
Deep belief network
Optimization design
Rolling bearing fault diagnosis
Salp swarm algorithm
Salp swarm algorithm
Deep belief network
Optimization design
Rolling bearing fault diagnosis
ISSN:0019-0578, 1879-2022, 1879-2022
Online-Zugang:Volltext
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Zusammenfassung:Due to the structure of rolling bearings and the complexity of the operating environment, collected vibration signals tend to show strong non-stationary and time-varying characteristics. Extracting useful fault feature information from actual bearing vibration signals and identifying bearing faults is challenging. In this paper, an innovative optimized adaptive deep belief network (SADBN) is proposed to address the problem of rolling bearing fault identification. The DBN is pre-trained by the minimum batch stochastic gradient descent. Then, a back propagation neural network and conjugate gradient descent are used to supervise and fine-tune the entire DBN model, which effectively improve the classification accuracy of the DBN. The salp swarm algorithm, an intelligent optimization method, is used to optimize the DBN. Then, the experience of deep learning network structure is summarized. Finally, a series of simulations based on the experimental data verify the effectiveness of the proposed method. •Propose a self-adaptive deep belief network.•Using Salp Swarm algorithm to optimize self-adaptive the deep belief network.•Propose a self-adaptive momentum strategy.•This method improves the classification efficiency of deep belief networks.
Bibliographie:ObjectType-Article-1
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ISSN:0019-0578
1879-2022
1879-2022
DOI:10.1016/j.isatra.2021.11.024