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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Vydáno v:	ISA transactions Ročník 128; číslo Pt B; s. 485 - 502
Hlavní autoři:	Gao, Shuzhi, Xu, Lintao, Zhang, Yimin, Pei, Zhiming
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 01.09.2022
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0019-0578 1879-2022 1879-2022
DOI:	10.1016/j.isatra.2021.11.024