Ming Lv, Changfeng Yan, Jianxiong Kang, Jiadong Meng, Zonggang Wang, Shengqiang Li, Bin Liu
{"title":"Multiple faults separation and identification of rolling bearings based on time-frequency spectrogram","authors":"Ming Lv, Changfeng Yan, Jianxiong Kang, Jiadong Meng, Zonggang Wang, Shengqiang Li, Bin Liu","doi":"10.1177/14759217231197110","DOIUrl":null,"url":null,"abstract":"Rolling bearings play a crucial role as components in rotating machinery across various industrial fields. Bearing faults can potentially lead to severe accidents in operating machines. Therefore, condition monitoring and fault diagnosis of rolling bearings are essential for preventing equipment failures. Multiple faults are a common occurrence resulting from the prolonged operation of rolling bearings, and numerous research efforts have been made to study multiple faults in different components of the bearing. However, diagnosing multiple faults in a single component of the rolling bearing still remains a highly challenging task. In this paper, a multiple faults separation and identification method based on time-frequency (TF) spectrogram (TFS) is proposed for vibration signals of rolling bearings. Firstly, the fast path optimization method is improved to match the TFS of original vibration signals in bearing faults generated by short-time Fourier transform. Then multiple TF curves are extracted from the TFS by the proposed multiple transient component curves extraction method based on the improved fast path optimization method. With the fault characteristic period, a classification criterion is introduced to separate TF curves. Secondly, a TF masking method is constructed to retain the TF information closely related to fault components of vibration signals. Finally, the novel TF representation can be obtained to develop signal reconstruction, and multiple faults can be detected based on envelope analysis separately. The experiments from rolling bearings with multiple faults on raceways are used to verify the effectiveness of the proposed methods.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":null,"pages":null},"PeriodicalIF":5.7000,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structural Health Monitoring-An International Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/14759217231197110","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Rolling bearings play a crucial role as components in rotating machinery across various industrial fields. Bearing faults can potentially lead to severe accidents in operating machines. Therefore, condition monitoring and fault diagnosis of rolling bearings are essential for preventing equipment failures. Multiple faults are a common occurrence resulting from the prolonged operation of rolling bearings, and numerous research efforts have been made to study multiple faults in different components of the bearing. However, diagnosing multiple faults in a single component of the rolling bearing still remains a highly challenging task. In this paper, a multiple faults separation and identification method based on time-frequency (TF) spectrogram (TFS) is proposed for vibration signals of rolling bearings. Firstly, the fast path optimization method is improved to match the TFS of original vibration signals in bearing faults generated by short-time Fourier transform. Then multiple TF curves are extracted from the TFS by the proposed multiple transient component curves extraction method based on the improved fast path optimization method. With the fault characteristic period, a classification criterion is introduced to separate TF curves. Secondly, a TF masking method is constructed to retain the TF information closely related to fault components of vibration signals. Finally, the novel TF representation can be obtained to develop signal reconstruction, and multiple faults can be detected based on envelope analysis separately. The experiments from rolling bearings with multiple faults on raceways are used to verify the effectiveness of the proposed methods.
期刊介绍:
Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.