{"title":"智能预测性维修综述:文献计量分析与新研究方向","authors":"V. Grubisic, J. F. Aguiar, Z. Simeu-Abazi","doi":"10.1109/ICCAD49821.2020.9260504","DOIUrl":null,"url":null,"abstract":"Industry 4.0 has brought a number of new technologies that are completely changing the shape of today’s maintenance processes. However, studies about these new technologies are still premature. This article provides an understanding and analysis of the most current and relevant studies on the area. As a method, we first selected the most relevant articles using a bibliometric methodology called the Theory of the Consolidated Meta-analytic Approach (TEMAC) method, then the 13 most significant studies were reviewed and presented and finally a study of possible gaps in the literature left uncovered by authors that allowed us to identify key elements and to forecast the future of Intelligent Predictive Maintenance.","PeriodicalId":270320,"journal":{"name":"2020 International Conference on Control, Automation and Diagnosis (ICCAD)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Review on Intelligent Predictive Maintenance: Bibliometric analysis and new research directions\",\"authors\":\"V. Grubisic, J. F. Aguiar, Z. Simeu-Abazi\",\"doi\":\"10.1109/ICCAD49821.2020.9260504\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Industry 4.0 has brought a number of new technologies that are completely changing the shape of today’s maintenance processes. However, studies about these new technologies are still premature. This article provides an understanding and analysis of the most current and relevant studies on the area. As a method, we first selected the most relevant articles using a bibliometric methodology called the Theory of the Consolidated Meta-analytic Approach (TEMAC) method, then the 13 most significant studies were reviewed and presented and finally a study of possible gaps in the literature left uncovered by authors that allowed us to identify key elements and to forecast the future of Intelligent Predictive Maintenance.\",\"PeriodicalId\":270320,\"journal\":{\"name\":\"2020 International Conference on Control, Automation and Diagnosis (ICCAD)\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Control, Automation and Diagnosis (ICCAD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCAD49821.2020.9260504\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Control, Automation and Diagnosis (ICCAD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAD49821.2020.9260504","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Review on Intelligent Predictive Maintenance: Bibliometric analysis and new research directions
Industry 4.0 has brought a number of new technologies that are completely changing the shape of today’s maintenance processes. However, studies about these new technologies are still premature. This article provides an understanding and analysis of the most current and relevant studies on the area. As a method, we first selected the most relevant articles using a bibliometric methodology called the Theory of the Consolidated Meta-analytic Approach (TEMAC) method, then the 13 most significant studies were reviewed and presented and finally a study of possible gaps in the literature left uncovered by authors that allowed us to identify key elements and to forecast the future of Intelligent Predictive Maintenance.