{"title":"Big-data-driven based intelligent prognostics scheme in industry 4.0 environment","authors":"Jihong Yan, Yue Meng, Lei Lu, Chao-zhong Guo","doi":"10.1109/PHM.2017.8079310","DOIUrl":null,"url":null,"abstract":"In this paper, a big-data-driven based intelligent prognostics strategy is proposed to deal with industrial big data generated in the process of intelligent manufacturing, which is an inevitable trend in the industry 4.0 environment. The developed scheme demonstrated the important issues for the intelligent prognostics methodology, including pre-processing methods for industrial big data, association analysis based feature processing, and deep learning based prognostics model, spark platform based parallel computing, etc. The proposed methodology and technical system will provide important referential value for the construction of big-data-driven machine prognostics system in industry 4.0 environment.","PeriodicalId":281875,"journal":{"name":"2017 Prognostics and System Health Management Conference (PHM-Harbin)","volume":"499 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Prognostics and System Health Management Conference (PHM-Harbin)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PHM.2017.8079310","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
Abstract
In this paper, a big-data-driven based intelligent prognostics strategy is proposed to deal with industrial big data generated in the process of intelligent manufacturing, which is an inevitable trend in the industry 4.0 environment. The developed scheme demonstrated the important issues for the intelligent prognostics methodology, including pre-processing methods for industrial big data, association analysis based feature processing, and deep learning based prognostics model, spark platform based parallel computing, etc. The proposed methodology and technical system will provide important referential value for the construction of big-data-driven machine prognostics system in industry 4.0 environment.