{"title":"Anomaly Detection of Storage Battery Based on Isolation Forest and Hyperparameter Tuning","authors":"Chun-Hsiang Lee, Xu Lu, X. Lin, Hongfeng Tao, Yaolei Xue, Chao Wu","doi":"10.1145/3395260.3395271","DOIUrl":null,"url":null,"abstract":"The safety of an uninterruptible power supply (UPS) unit is very important in the operation of a telecommunication room. It is necessary to identify and replace abnormal electrical batteries of the UPS to ensure the normal operation of the equipment. In this paper, a single-model method based on isolation forest and hyperparameter tuning is proposed for detecting abnormal batteries. Experimental results show that the proposed method is efficient in offline situations. A multi-model method is also proposed to deal with the online anomaly detection problem, which is found performing well.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3395260.3395271","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The safety of an uninterruptible power supply (UPS) unit is very important in the operation of a telecommunication room. It is necessary to identify and replace abnormal electrical batteries of the UPS to ensure the normal operation of the equipment. In this paper, a single-model method based on isolation forest and hyperparameter tuning is proposed for detecting abnormal batteries. Experimental results show that the proposed method is efficient in offline situations. A multi-model method is also proposed to deal with the online anomaly detection problem, which is found performing well.