Lianhui Jia, Lijie Jiang, Yongliang Wen, Hongchao Wang
{"title":"Weak Fault Feature Extraction for Rolling Element Bearing Based on a Two-Stage Method","authors":"Lianhui Jia, Lijie Jiang, Yongliang Wen, Hongchao Wang","doi":"10.1155/2023/6671730","DOIUrl":null,"url":null,"abstract":"Timely and effective feature extraction is the key for fault diagnosis of rolling element bearing (REB). However, fault feature extraction will become very difficult in the early weak fault stage of REB due to the interference of strong background noise. To solve the above difficulty, a two-stage feature extraction method for early weak fault of REB is proposed, which mainly combines feature mode decomposition (FMD) with a blind deconvolution (BD) method. Firstly, based on the impulsiveness and cyclostationary characteristics of the vibration signal of faulty REB, FMD is used to decompose the complex original vibration signal into several modes containing single component. Subsequently, the sparse index (SI) is calculated for each mode, and the mode containing sensitive fault feature is selected for further analysis. Subsequently, apply the deconvolution method on the selected mode for further enhancing the impulsive characteristic. At last, traditional envelope spectrum (ES) analysis is applied on the filtered signal, and satisfactory fault features are extracted. Effectiveness and advantages of the proposed method are verified through experimental and engineering signals of REBs.","PeriodicalId":50327,"journal":{"name":"International Journal of Distributed Sensor Networks","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Distributed Sensor Networks","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1155/2023/6671730","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Timely and effective feature extraction is the key for fault diagnosis of rolling element bearing (REB). However, fault feature extraction will become very difficult in the early weak fault stage of REB due to the interference of strong background noise. To solve the above difficulty, a two-stage feature extraction method for early weak fault of REB is proposed, which mainly combines feature mode decomposition (FMD) with a blind deconvolution (BD) method. Firstly, based on the impulsiveness and cyclostationary characteristics of the vibration signal of faulty REB, FMD is used to decompose the complex original vibration signal into several modes containing single component. Subsequently, the sparse index (SI) is calculated for each mode, and the mode containing sensitive fault feature is selected for further analysis. Subsequently, apply the deconvolution method on the selected mode for further enhancing the impulsive characteristic. At last, traditional envelope spectrum (ES) analysis is applied on the filtered signal, and satisfactory fault features are extracted. Effectiveness and advantages of the proposed method are verified through experimental and engineering signals of REBs.
期刊介绍:
International Journal of Distributed Sensor Networks (IJDSN) is a JCR ranked, peer-reviewed, open access journal that focuses on applied research and applications of sensor networks. The goal of this journal is to provide a forum for the publication of important research contributions in developing high performance computing solutions to problems arising from the complexities of these sensor network systems. Articles highlight advances in uses of sensor network systems for solving computational tasks in manufacturing, engineering and environmental systems.