4.4 Article

A Novel Fault Diagnosis Approach for Rolling Bearing Based on CWT and Adaptive Sparse Representation

期刊

SHOCK AND VIBRATION
卷 2022, 期 -, 页码 -

出版社

HINDAWI LTD
DOI: 10.1155/2022/9079790

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资金

  1. Key-Area Research and Development Program of Guangdong Province [2020B090927002]
  2. National Natural Science Funds of Shaanxi Province [2021JM-017]
  3. National Natural Science Foundation of China [51975462]

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This study proposes an adaptive sparse representation (ASR) method based on TQWT, which integrates SCS and parameter optimization for fault feature extraction. Due to weak fault symptoms and background noise interference, a fault diagnosis strategy based on CWT and ASR is also investigated. Simulated and experimental results verify the effectiveness of the proposed method in accurately extracting weak impulse features from the noise environment.
Extraction and enhancement of weak impulse signature is the key of rolling bearing fault prognostics in which case the features are often weak and covered by noise. Tunable Q-factor wavelet transform (TQWT), as an emerging wavelet construction theory developed in a frequency domain explicitly, has the advantages of matching with the specific oscillation behavior of signal components. In this article, an adaptive sparse representation (ASR) method is proposed, which integrates the sparse code shrinkage (SCS) and parameter optimization into TQWT. However, direct application of ASR is difficult to extract fault signatures at the early stage or low-speed operation due to weak fault symptoms and background noise. A novel fault diagnosis strategy based on continuous wavelet transform (CWT) and ASR is investigated. CWT owns significant advantages on multiscale subdivision and weak signal detection. The results of simulated and experimental vibration signal analyses verify the effectiveness of the proposed method in accurately extracting weak impulse features from the noise environment.

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