{"title":"CARIMA模型的粘着故障检测方法","authors":"T. Tanikawa, Henmi Tomohiro","doi":"10.5954/ICAROB.2018.OS3-7","DOIUrl":null,"url":null,"abstract":"If a system with a fault continues to be operated, it can cause a serious accident or a considerable damage. Thus, it is important to detect faults and compensate them, and many fault detection methods have been proposed [1,2]. The advantage of fault detection is that the safety is improved, you can cope with the fault more promptly, and sometimes the system can be controlled compensating the fault. There are two kinds in fault detection, signal-based detection and model-based one. The signal-based detection is for example a method using spectral analysis, statistical signal analysis or pattern recognition, while the modelbased detection uses an observer or a parameter estimation [3]. In modelbased detection, a general method detecting additive faults is proposed by Isermann [4].","PeriodicalId":43148,"journal":{"name":"Journal of Robotics Networking and Artificial Life","volume":"64 1","pages":"149-152"},"PeriodicalIF":0.3000,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sticking Fault Detecting Method for CARIMA Model\",\"authors\":\"T. Tanikawa, Henmi Tomohiro\",\"doi\":\"10.5954/ICAROB.2018.OS3-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"If a system with a fault continues to be operated, it can cause a serious accident or a considerable damage. Thus, it is important to detect faults and compensate them, and many fault detection methods have been proposed [1,2]. The advantage of fault detection is that the safety is improved, you can cope with the fault more promptly, and sometimes the system can be controlled compensating the fault. There are two kinds in fault detection, signal-based detection and model-based one. The signal-based detection is for example a method using spectral analysis, statistical signal analysis or pattern recognition, while the modelbased detection uses an observer or a parameter estimation [3]. In modelbased detection, a general method detecting additive faults is proposed by Isermann [4].\",\"PeriodicalId\":43148,\"journal\":{\"name\":\"Journal of Robotics Networking and Artificial Life\",\"volume\":\"64 1\",\"pages\":\"149-152\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2018-02-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Robotics Networking and Artificial Life\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5954/ICAROB.2018.OS3-7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Robotics Networking and Artificial Life","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5954/ICAROB.2018.OS3-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ROBOTICS","Score":null,"Total":0}
If a system with a fault continues to be operated, it can cause a serious accident or a considerable damage. Thus, it is important to detect faults and compensate them, and many fault detection methods have been proposed [1,2]. The advantage of fault detection is that the safety is improved, you can cope with the fault more promptly, and sometimes the system can be controlled compensating the fault. There are two kinds in fault detection, signal-based detection and model-based one. The signal-based detection is for example a method using spectral analysis, statistical signal analysis or pattern recognition, while the modelbased detection uses an observer or a parameter estimation [3]. In modelbased detection, a general method detecting additive faults is proposed by Isermann [4].