{"title":"基于脊的柔性薄壁轴承通用同步压缩变换故障诊断。","authors":"Yanjiang Yu, Xuezhi Zhao","doi":"10.1016/j.isatra.2025.08.040","DOIUrl":null,"url":null,"abstract":"<p><p>Accurate fault diagnosis in flexible thin-wall bearings is crucial for harmonic drive reliability but remains challenging, as fault impulses are often masked by strong operational vibrations. In response to this challenge, a ridge-based general synchrosqueezing transform (RGST) is proposed in this paper. This method unifies time-frequency analysis by operating at the ridge level, using energy trajectories extracted from both instantaneous frequency and group delay estimators. Key features of RGST include a binary ridge expansion mask to enhance energy concentration and suppress noise, and an agglomerative clustering algorithm to separate signal components. Experimental results demonstrate that RGST achieves a concentrated time-frequency representation with superior component separation and noise robustness, thereby improving the reliability of fault diagnosis under multiple fault conditions in flexible thin-wall bearings.</p>","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5000,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ridge-based general synchrosqueezing transform for flexible thin-wall bearing fault diagnosis.\",\"authors\":\"Yanjiang Yu, Xuezhi Zhao\",\"doi\":\"10.1016/j.isatra.2025.08.040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Accurate fault diagnosis in flexible thin-wall bearings is crucial for harmonic drive reliability but remains challenging, as fault impulses are often masked by strong operational vibrations. In response to this challenge, a ridge-based general synchrosqueezing transform (RGST) is proposed in this paper. This method unifies time-frequency analysis by operating at the ridge level, using energy trajectories extracted from both instantaneous frequency and group delay estimators. Key features of RGST include a binary ridge expansion mask to enhance energy concentration and suppress noise, and an agglomerative clustering algorithm to separate signal components. Experimental results demonstrate that RGST achieves a concentrated time-frequency representation with superior component separation and noise robustness, thereby improving the reliability of fault diagnosis under multiple fault conditions in flexible thin-wall bearings.</p>\",\"PeriodicalId\":94059,\"journal\":{\"name\":\"ISA transactions\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":6.5000,\"publicationDate\":\"2025-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISA transactions\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.isatra.2025.08.040\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISA transactions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.isatra.2025.08.040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ridge-based general synchrosqueezing transform for flexible thin-wall bearing fault diagnosis.
Accurate fault diagnosis in flexible thin-wall bearings is crucial for harmonic drive reliability but remains challenging, as fault impulses are often masked by strong operational vibrations. In response to this challenge, a ridge-based general synchrosqueezing transform (RGST) is proposed in this paper. This method unifies time-frequency analysis by operating at the ridge level, using energy trajectories extracted from both instantaneous frequency and group delay estimators. Key features of RGST include a binary ridge expansion mask to enhance energy concentration and suppress noise, and an agglomerative clustering algorithm to separate signal components. Experimental results demonstrate that RGST achieves a concentrated time-frequency representation with superior component separation and noise robustness, thereby improving the reliability of fault diagnosis under multiple fault conditions in flexible thin-wall bearings.