{"title":"机器学习的预测性维护应用","authors":"B. Cline, R. Niculescu, D. Huffman, Bob Deckel","doi":"10.1109/RAM.2017.7889679","DOIUrl":null,"url":null,"abstract":"Machine Learning provides a complementary approach to maintenance planning by analyzing significant data sets of individual machine performance and environment variables, identifying failure signatures and profiles, and providing an actionable prediction of failure for individual parts.","PeriodicalId":138871,"journal":{"name":"2017 Annual Reliability and Maintainability Symposium (RAMS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"40","resultStr":"{\"title\":\"Predictive maintenance applications for machine learning\",\"authors\":\"B. Cline, R. Niculescu, D. Huffman, Bob Deckel\",\"doi\":\"10.1109/RAM.2017.7889679\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Machine Learning provides a complementary approach to maintenance planning by analyzing significant data sets of individual machine performance and environment variables, identifying failure signatures and profiles, and providing an actionable prediction of failure for individual parts.\",\"PeriodicalId\":138871,\"journal\":{\"name\":\"2017 Annual Reliability and Maintainability Symposium (RAMS)\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"40\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Annual Reliability and Maintainability Symposium (RAMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAM.2017.7889679\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Annual Reliability and Maintainability Symposium (RAMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAM.2017.7889679","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predictive maintenance applications for machine learning
Machine Learning provides a complementary approach to maintenance planning by analyzing significant data sets of individual machine performance and environment variables, identifying failure signatures and profiles, and providing an actionable prediction of failure for individual parts.