{"title":"多变量加权Tsallis斜率熵用于滚动轴承故障诊断","authors":"Yuxing Li , Jingyi Li , Yan Yan","doi":"10.1016/j.apacoust.2025.110914","DOIUrl":null,"url":null,"abstract":"<div><div>To address the issue of neglecting partial amplitude information in fault classification using multivariate slope entropy (mvSloEN), we proposed an improved method: multivariate weighted slope Tsallis entropy (mvWTSloEN). This method introduces a weight factor to emphasize amplitude fluctuations and incorporates Tsallis entropy, thereby enhancing the description of signal complexity. Five simulation experiments highlight the robustness, sensitivity, and enhanced classification performance of mvWTSloEN. Additionally, experiments with two real-world datasets confirm its superior ability to distinguish different types of bearing fault signals. In conclusion, mvWTSloEN effectively enhances the feature extraction capabilities for nonlinear signals, offering a more accurate and robust method for signal analysis.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":"240 ","pages":"Article 110914"},"PeriodicalIF":3.4000,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multivariate weighted Tsallis slope entropy for rolling bearing fault diagnosis\",\"authors\":\"Yuxing Li , Jingyi Li , Yan Yan\",\"doi\":\"10.1016/j.apacoust.2025.110914\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>To address the issue of neglecting partial amplitude information in fault classification using multivariate slope entropy (mvSloEN), we proposed an improved method: multivariate weighted slope Tsallis entropy (mvWTSloEN). This method introduces a weight factor to emphasize amplitude fluctuations and incorporates Tsallis entropy, thereby enhancing the description of signal complexity. Five simulation experiments highlight the robustness, sensitivity, and enhanced classification performance of mvWTSloEN. Additionally, experiments with two real-world datasets confirm its superior ability to distinguish different types of bearing fault signals. In conclusion, mvWTSloEN effectively enhances the feature extraction capabilities for nonlinear signals, offering a more accurate and robust method for signal analysis.</div></div>\",\"PeriodicalId\":55506,\"journal\":{\"name\":\"Applied Acoustics\",\"volume\":\"240 \",\"pages\":\"Article 110914\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Acoustics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0003682X2500386X\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ACOUSTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Acoustics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0003682X2500386X","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
Multivariate weighted Tsallis slope entropy for rolling bearing fault diagnosis
To address the issue of neglecting partial amplitude information in fault classification using multivariate slope entropy (mvSloEN), we proposed an improved method: multivariate weighted slope Tsallis entropy (mvWTSloEN). This method introduces a weight factor to emphasize amplitude fluctuations and incorporates Tsallis entropy, thereby enhancing the description of signal complexity. Five simulation experiments highlight the robustness, sensitivity, and enhanced classification performance of mvWTSloEN. Additionally, experiments with two real-world datasets confirm its superior ability to distinguish different types of bearing fault signals. In conclusion, mvWTSloEN effectively enhances the feature extraction capabilities for nonlinear signals, offering a more accurate and robust method for signal analysis.
期刊介绍:
Since its launch in 1968, Applied Acoustics has been publishing high quality research papers providing state-of-the-art coverage of research findings for engineers and scientists involved in applications of acoustics in the widest sense.
Applied Acoustics looks not only at recent developments in the understanding of acoustics but also at ways of exploiting that understanding. The Journal aims to encourage the exchange of practical experience through publication and in so doing creates a fund of technological information that can be used for solving related problems. The presentation of information in graphical or tabular form is especially encouraged. If a report of a mathematical development is a necessary part of a paper it is important to ensure that it is there only as an integral part of a practical solution to a problem and is supported by data. Applied Acoustics encourages the exchange of practical experience in the following ways: • Complete Papers • Short Technical Notes • Review Articles; and thereby provides a wealth of technological information that can be used to solve related problems.
Manuscripts that address all fields of applications of acoustics ranging from medicine and NDT to the environment and buildings are welcome.