{"title":"A similarity-guided block-structured dictionary learning method for fault feature extraction of rolling bearings","authors":"Jimeng Li, Qingxin Shi, Xilei Guan, Zong Meng","doi":"10.1016/j.sigpro.2025.110195","DOIUrl":null,"url":null,"abstract":"<div><div>Periodical impulses generated by locally damaged bearings are frequently drowned out by harmonics and noise interference, making it challenging to accurately detect rolling bearing faults. Dictionary learning-based sparse representation provides an effective way to accurately recover fault impulses from noisy signals. However, current dictionary learning algorithms still have some deficiencies, such as low computational efficiency and insufficient accuracy of signal reconstruction. How to obtain an overcomplete dictionary whose atoms match the fault features and have low coherence between atoms is still an open problem. Therefore, a sparse representation method via similarity-guided block-structured dictionary learning is investigated to extract periodic impulse features for fault detection of rolling bearings. First, an initial signal matrix is constructed by performing period segmentation and cyclic shift operations on the original signal, and then the harmonics are eliminated column by column by least square trend estimation algorithm. Secondly, a block-structured dictionary learning method based on K-SVD is designed by using the similarity between atoms, which makes the atoms in a block as similar as possible and the atoms between blocks as different as possible, which in turn significantly improves the efficiency and accuracy of signal recovery. Finally, the block-structured dictionary optimized by soft threshold shrinkage is applied to recover the periodic impulse signal from the initial signal matrix to identify rolling bearing faults. Numerical simulation and analysis results of rolling bearing fault signals reveal that the method suggested can recover periodic impulse signals more accurately, and its denoising performance is better than some existing comparison methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110195"},"PeriodicalIF":3.4000,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165168425003093","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Periodical impulses generated by locally damaged bearings are frequently drowned out by harmonics and noise interference, making it challenging to accurately detect rolling bearing faults. Dictionary learning-based sparse representation provides an effective way to accurately recover fault impulses from noisy signals. However, current dictionary learning algorithms still have some deficiencies, such as low computational efficiency and insufficient accuracy of signal reconstruction. How to obtain an overcomplete dictionary whose atoms match the fault features and have low coherence between atoms is still an open problem. Therefore, a sparse representation method via similarity-guided block-structured dictionary learning is investigated to extract periodic impulse features for fault detection of rolling bearings. First, an initial signal matrix is constructed by performing period segmentation and cyclic shift operations on the original signal, and then the harmonics are eliminated column by column by least square trend estimation algorithm. Secondly, a block-structured dictionary learning method based on K-SVD is designed by using the similarity between atoms, which makes the atoms in a block as similar as possible and the atoms between blocks as different as possible, which in turn significantly improves the efficiency and accuracy of signal recovery. Finally, the block-structured dictionary optimized by soft threshold shrinkage is applied to recover the periodic impulse signal from the initial signal matrix to identify rolling bearing faults. Numerical simulation and analysis results of rolling bearing fault signals reveal that the method suggested can recover periodic impulse signals more accurately, and its denoising performance is better than some existing comparison methods.
期刊介绍:
Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing.
Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.