Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples

Document identifier: oai:DiVA.org:ltu-77568
Access full text here:10.1016/j.knosys.2019.105313
Keyword: Engineering and Technology, Civil Engineering, Other Civil Engineering, Teknik och teknologier, Samhällsbyggnadsteknik, Annan samhällsbyggnadsteknik, Deep transfer multi-wavelet auto-encode, Gearbox fault, Transfer diagnosis, Variable working conditions, Few target training samples, Drift och underhållsteknik, Operation and Maintenance
Publication year: 2020
Abstract:

Lack of typical fault samples remains a huge challenge for intelligent fault diagnosis of gearbox. In this paper, a novel approach named deep transfer multi-wavelet auto-encoder is presented for gearbox intelligent fault diagnosis with few training samples. Firstly, new-type deep multi-wavelet auto-encoder is designed for learning important features of the collected vibration signals of gearbox. Secondly, high-quality auxiliary samples are selected based on similarity measure to well pre-train a source model sharing similar characteristics with the target domain. Thirdly, parameter knowledge acquired from the source model is transferred to target model using very few target training samples. Transfer diagnosis cases for different fault severities and compound faults of gearbox confirm the feasibility of the proposed approach even if the working conditions have significant changes.

Authors

Zhiyi He

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University
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Haidong Shao

Luleå tekniska universitet; Drift, underhåll och akustik; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University
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Ping Wang

AECC Hunan Aviation Powerplant Research Institute. AECC Key Laboratory of Aero-engine Vibration Technology
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Jing (Janet) Lin

Luleå tekniska universitet; Drift, underhåll och akustik
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Junsheng Cheng

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University
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Yu Yang

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University
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