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

• Published in: ISA transactions Vol. 105; pp. 308 - 319
• Main Authors: Haidong, Shao, Ziyang, Ding, Junsheng, Cheng, Hongkai, Jiang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2020
• Subjects: Different rotating machines; Drift och underhållsteknik; Intelligent fault diagnosis; Novel stacked transfer auto-encoder; Operation and Maintenance; Parameter transfer strategy; Particle swarm optimization; Novel stacked transfer auto-encoder; Different rotating machines; Parameter transfer strategy; Intelligent fault diagnosis; Particle swarm optimization
• ISSN: 0019-0578; 1879-2022; 1879-2022
• DOI: 10.1016/j.isatra.2020.05.041

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. [Display omitted] •Three advanced skills are combined to design novel stacked auto-encoder (NSAE).•Novel stacked transfer auto-encoder (NSTAE) is built with parameter transfer.•PSO is used to flexibly decide multiple hyperparameters of NSTAE.•Transfer diagnosis cases of bearing and gear are used to test the presented method.