期刊
KNOWLEDGE-BASED SYSTEMS
卷 191, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2019.105313
关键词
Deep transfer multi-wavelet auto-encode; Gearbox fault; Transfer diagnosis; Variable working conditions; Few target training samples
资金
- National Natural Science Foundation of China [51875183, 51905160]
- Fundamental Research Funds for the Central Universities, China [53111 8010335]
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. (C) 2019 Elsevier B.V. All rights reserved.
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