Hiroki Hayasaka, Kaisei Kanetani, Sohei Nakashima, M. Yamazaki, T. Babasaki, Risshi Kondo, Masami Amano
{"title":"Method for detection of lot defects for maintenance of ICT power supplies and air conditioning equipment and verification results","authors":"Hiroki Hayasaka, Kaisei Kanetani, Sohei Nakashima, M. Yamazaki, T. Babasaki, Risshi Kondo, Masami Amano","doi":"10.1109/INTLEC.2017.8214122","DOIUrl":null,"url":null,"abstract":"In this paper, we report on a method of finding lot defects and the results of a verification related to the maintenance of power-supply and air-conditioning equipment for ICT. We built a logic and models to automatically detect equipment that may be included in a defective lot. An equipment anomaly analysis system was constructed. For verification, actual past lot failure equipment was applied to the logic and models for comparison with the conventional method. As a result, detection of lot defects became faster in 3 out of 4 cases. Lot defects were generally detected at an early stage. In addition, the result of operating the equipment anomaly analysis system is described.","PeriodicalId":366207,"journal":{"name":"2017 IEEE International Telecommunications Energy Conference (INTELEC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Telecommunications Energy Conference (INTELEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INTLEC.2017.8214122","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, we report on a method of finding lot defects and the results of a verification related to the maintenance of power-supply and air-conditioning equipment for ICT. We built a logic and models to automatically detect equipment that may be included in a defective lot. An equipment anomaly analysis system was constructed. For verification, actual past lot failure equipment was applied to the logic and models for comparison with the conventional method. As a result, detection of lot defects became faster in 3 out of 4 cases. Lot defects were generally detected at an early stage. In addition, the result of operating the equipment anomaly analysis system is described.