{"title":"工业4.0背景下的预测性管理","authors":"M. Krynke","doi":"10.2478/cqpi-2021-0005","DOIUrl":null,"url":null,"abstract":"Abstract The article presents information on the potential of Industry 4.0 in the field of maintenance. This work explores the potential and trends of predictive maintenance management in an industrial big data environment. The development of predictive maintenance, its technical challenges and in the context of Industry 4.0 was presented. In addition, a case study that illustrates how maintenance management and predictive maintenance can be applied to the maintenance of wind turbines is discussed.","PeriodicalId":166707,"journal":{"name":"Conference Quality Production Improvement – CQPI","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predictive Management in the Context of Industry 4.0\",\"authors\":\"M. Krynke\",\"doi\":\"10.2478/cqpi-2021-0005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The article presents information on the potential of Industry 4.0 in the field of maintenance. This work explores the potential and trends of predictive maintenance management in an industrial big data environment. The development of predictive maintenance, its technical challenges and in the context of Industry 4.0 was presented. In addition, a case study that illustrates how maintenance management and predictive maintenance can be applied to the maintenance of wind turbines is discussed.\",\"PeriodicalId\":166707,\"journal\":{\"name\":\"Conference Quality Production Improvement – CQPI\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Conference Quality Production Improvement – CQPI\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/cqpi-2021-0005\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference Quality Production Improvement – CQPI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/cqpi-2021-0005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predictive Management in the Context of Industry 4.0
Abstract The article presents information on the potential of Industry 4.0 in the field of maintenance. This work explores the potential and trends of predictive maintenance management in an industrial big data environment. The development of predictive maintenance, its technical challenges and in the context of Industry 4.0 was presented. In addition, a case study that illustrates how maintenance management and predictive maintenance can be applied to the maintenance of wind turbines is discussed.
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