{"title":"Permanent magnet synchronous machines position sensor failure identification using sequence mining","authors":"Safa Aloui Dkhil, M. Bennani, H. B. A. Sethom","doi":"10.1109/ICCAD55197.2022.9854032","DOIUrl":null,"url":null,"abstract":"In recent years, Permanent Magnet Synchronous Motors (PMSMs) are increasingly used in industrial applications, automotive, aerospace, robotics, etc. However, this type of actuator can be affected by faults and failures which make it operate in severe conditions. An undetected fault in the PMSM-based system may lead to high repair costs or even catastrophic failure. Therefore, each fault must be detected and isolated at early stages to avoid its extensive effects on the motor. PMSM sensors are sensitive to many types of faults, such as the total loss of the position/speed information.This paper shows the efficiency of sequence mining and data dependency analysis to identify faults localization at design time. The detection of the position sensor failure has been carried out from gathering the PMSM currents data.","PeriodicalId":436377,"journal":{"name":"2022 International Conference on Control, Automation and Diagnosis (ICCAD)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Control, Automation and Diagnosis (ICCAD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAD55197.2022.9854032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In recent years, Permanent Magnet Synchronous Motors (PMSMs) are increasingly used in industrial applications, automotive, aerospace, robotics, etc. However, this type of actuator can be affected by faults and failures which make it operate in severe conditions. An undetected fault in the PMSM-based system may lead to high repair costs or even catastrophic failure. Therefore, each fault must be detected and isolated at early stages to avoid its extensive effects on the motor. PMSM sensors are sensitive to many types of faults, such as the total loss of the position/speed information.This paper shows the efficiency of sequence mining and data dependency analysis to identify faults localization at design time. The detection of the position sensor failure has been carried out from gathering the PMSM currents data.