Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO

Shao Haidong¹, Ding Ziyang², Cheng Junsheng², Jiang Hongkai³

Affiliations

¹ State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China; Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Luleå 97187, Sweden. Electronic address: hdshao@hnu.edu.cn.
² State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
³ School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China.

PMID: 32473735
DOI: 10.1016/j.isatra.2020.05.041

Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO

Shao Haidong et al. ISA Trans. 2020 Oct.

. 2020 Oct:105:308-319.

doi: 10.1016/j.isatra.2020.05.041. Epub 2020 May 26.

Authors

Shao Haidong¹, Ding Ziyang², Cheng Junsheng², Jiang Hongkai³

Affiliations

¹ State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China; Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Luleå 97187, Sweden. Electronic address: hdshao@hnu.edu.cn.
² State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
³ School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China.

PMID: 32473735
DOI: 10.1016/j.isatra.2020.05.041

Abstract

Intelligent fault diagnosis techniques cross rotating machines have great significances in theory and engineering For this purpose, this paper presents a novel method using novel stacked transfer auto-encoder (NSTAE) optimized by particle swarm optimization (PSO). First, novel stacked auto-encoder (NSAE) model is designed with scaled exponential linear unit (SELU), correntropy and nonnegative constraint. Then, NSTAE is constructed using NSAE and parameter transfer strategy to enable the pre-trained source-domain NSAE to adapt to the target-domain samples. Finally, PSO is used to flexibly decide the hyperparameters of NSTAE. The effectiveness and superiority of the presented method are investigated through analyzing the collected experimental data of bearings and gears from different rotating machines.

Keywords: Different rotating machines; Intelligent fault diagnosis; Novel stacked transfer auto-encoder; Parameter transfer strategy; Particle swarm optimization.

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Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO

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Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO

Authors

Affiliations

Abstract

Conflict of interest statement

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