{"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}
引用次数: 40
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.
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