Bearing incipient fault feature extraction using adaptive period matching enhanced sparse representation

Renhe Yao, Hongkai Jiang, Xingqiu Li, Jiping Cao

科研成果: 期刊稿件文章同行评审

61 引用 (Scopus)

摘要

Bearing incipient fault feature extraction is crucial and challenging throughout its life cycle. In this paper, an adaptive period matching enhanced sparse representation (APMESR) algorithm is developed to address this issue. First, a novel methodology for estimating the period of faulty impulses is proposed from the perspective of mining the periodicity-related numerical patterns. Second, the period estimation methodology is embedded in a sparse representation model to implement adaptive period matching to form APMESR, which is capable of achieving periodic sparsity. Third, maximal overlap discrete wavelet packet transform is selected as the linear transformation of APMESR for improving its ability to reduce noise and highlight periodic impulse signatures. Furthermore, evaluations and comparisons are conducted using simulations to demonstrate the validity and performance of the proposed period estimation methodology, linear transformation, and APMESR. Experimental results indicate that APMESR can effectively extract incipient bearing fault features and outperforms other well-advanced methods.

源语言英语
文章编号108467
期刊Mechanical Systems and Signal Processing
166
DOI
出版状态已出版 - 1 3月 2022

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