{"title":"机械加工过程控制的故障检测辅助系统","authors":"X.L. Kong , D.C.H. Yang , H.L. Li","doi":"10.1016/0378-3804(89)90033-8","DOIUrl":null,"url":null,"abstract":"<div><p>Based on Kalman filter and probability theories, a failure detection model as well as a monitoring supporting system have been developed for machining process control and monitoring. Experimental results from computer simulations show that the model and the supporting system are effective and the average time in detecting typical failure signals is reduced about 20 percent when utilizing the supporting system.</p></div>","PeriodicalId":100801,"journal":{"name":"Journal of Mechanical Working Technology","volume":"20 ","pages":"Pages 229-236"},"PeriodicalIF":0.0000,"publicationDate":"1989-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0378-3804(89)90033-8","citationCount":"0","resultStr":"{\"title\":\"A failure detection supporting system for machining process control\",\"authors\":\"X.L. Kong , D.C.H. Yang , H.L. Li\",\"doi\":\"10.1016/0378-3804(89)90033-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Based on Kalman filter and probability theories, a failure detection model as well as a monitoring supporting system have been developed for machining process control and monitoring. Experimental results from computer simulations show that the model and the supporting system are effective and the average time in detecting typical failure signals is reduced about 20 percent when utilizing the supporting system.</p></div>\",\"PeriodicalId\":100801,\"journal\":{\"name\":\"Journal of Mechanical Working Technology\",\"volume\":\"20 \",\"pages\":\"Pages 229-236\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1989-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/0378-3804(89)90033-8\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Mechanical Working Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/0378380489900338\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mechanical Working Technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/0378380489900338","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A failure detection supporting system for machining process control
Based on Kalman filter and probability theories, a failure detection model as well as a monitoring supporting system have been developed for machining process control and monitoring. Experimental results from computer simulations show that the model and the supporting system are effective and the average time in detecting typical failure signals is reduced about 20 percent when utilizing the supporting system.
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