{"title":"基于灰色AR组合模型的发动机状态监测","authors":"Qiang Wang, Sheng Hui Dai","doi":"10.1109/CESCE.2010.19","DOIUrl":null,"url":null,"abstract":"Aiming at the problems of the wear condition monitoring, grey theory and auto-regressive combination forecasting model was put forward, and the combination model was build. The rough trend of the wear particle content change can be reflected through grey theory, and the detail of the change can be reflected through auto-regressive model. By testing and comparing a set of Ferro graphic data, the result shows that the combination model has a better forecasting result.","PeriodicalId":6371,"journal":{"name":"2010 International Conference on Challenges in Environmental Science and Computer Engineering","volume":"74 1","pages":"215-218"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Engine Condition Monitoring Based on Grey AR Combination Model\",\"authors\":\"Qiang Wang, Sheng Hui Dai\",\"doi\":\"10.1109/CESCE.2010.19\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the problems of the wear condition monitoring, grey theory and auto-regressive combination forecasting model was put forward, and the combination model was build. The rough trend of the wear particle content change can be reflected through grey theory, and the detail of the change can be reflected through auto-regressive model. By testing and comparing a set of Ferro graphic data, the result shows that the combination model has a better forecasting result.\",\"PeriodicalId\":6371,\"journal\":{\"name\":\"2010 International Conference on Challenges in Environmental Science and Computer Engineering\",\"volume\":\"74 1\",\"pages\":\"215-218\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-03-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Challenges in Environmental Science and Computer Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CESCE.2010.19\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Challenges in Environmental Science and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CESCE.2010.19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Engine Condition Monitoring Based on Grey AR Combination Model
Aiming at the problems of the wear condition monitoring, grey theory and auto-regressive combination forecasting model was put forward, and the combination model was build. The rough trend of the wear particle content change can be reflected through grey theory, and the detail of the change can be reflected through auto-regressive model. By testing and comparing a set of Ferro graphic data, the result shows that the combination model has a better forecasting result.
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