TY - JOUR
T1 - Bearing incipient fault feature extraction using adaptive period matching enhanced sparse representation
AU - Yao, Renhe
AU - Jiang, Hongkai
AU - Li, Xingqiu
AU - Cao, Jiping
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/3/1
Y1 - 2022/3/1
N2 - 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.
AB - 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.
KW - Adaptive period matching
KW - Bearing incipient fault feature extraction
KW - Enhanced sparse representation
KW - Period estimation methodology
UR - http://www.scopus.com/inward/record.url?scp=85115992162&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2021.108467
DO - 10.1016/j.ymssp.2021.108467
M3 - 文章
AN - SCOPUS:85115992162
SN - 0888-3270
VL - 166
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
M1 - 108467
ER -