期刊
MEASUREMENT
卷 182, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2021.109718
关键词
Fault pulse extraction; Feature enhancement; Multi-scale dictionary learning; Frequency spectrum
资金
- National Natural Science Foundation of China [51905452]
- China Postdoctoral Science Foundation [2019M663549]
- Local Development Foundation guided by the Central Government [2020ZYD012]
- Planning Project of Science & Technology Department of Sichuan Province [2019YFG0353]
This paper proposes a fault pulse extraction and feature enhancement method for bearing fault diagnosis. By using the multi-scale alternating direction multiplier method for dictionary learning to extract fault impact signal and frequency spectrum averaging to enhance bearing fault characteristic frequency, the feasibility of this method in bearing fault diagnosis is verified through numerical simulation and rail transit transmission failure simulation experimental analysis.
Generally, the transient characteristics of early bearing failure are not obvious. How to extract weak transient features is a big challenge. Dictionary learning has been successfully used to extract bearing fault features. However, the traditional dictionary learning is easy to fall into local optimum and cannot extract fault features from complex signals. And it often consumes huge computational costs. In order to solve the above problems, this paper proposes a fault pulse extraction and feature enhancement method for bearing fault diagnosis. Firstly, the bearing vibration signal is segmented in the time domain. Then this paper proposes a multi-scale alternating direction multiplier method for dictionary learning (MADMMDL) to extract fault impact signal from the segment signal. Finally, frequency spectrum averaging is used to enhance the bearing fault characteristic frequency. Through numerical simulation and rail transit transmission failure simulation experimental analysis, the feasi-bility of this method in bearing fault diagnosis is verified.
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