文章基本信息

标题：Predictive Maintenance of VRLA Batteries in UPS towards Reliable Data Centers ⁎
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作者：Jing-Xian Tang ; Jin-Hong Du ; Yiting Lin 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：13607-13612
DOI：10.1016/j.ifacol.2020.12.854
语种：English
出版社：Elsevier
摘要：AbstractThe reliability of data centers can be severely affected when battery failure occurs in the Uninterruptible Power Supply (UPS). Thus it has become a central issue for the industry to discover failure-impending batteries in UPS. In this paper, we consider this important problem and present a data-driven method for predictive battery maintenance. The major contributions are as follows.First, we develop a changepoint detection technique for efficient data labeling. Second, new features are designed to fully utilize the dataset. Third, we build a predictive classification model which can discriminate between healthy and failure-impending batteries. Our method has been built and evaluated on 209,912,615 records from Tencent data center involving nearly 300 batteries monitored over 2 years. The experiment on test set shows that our method is able to predict battery replacement with 98% accuracy and averagely 15 days in advance, which outperforms the previous maintenance policy by more than 8%.
关键词：KeywordsPredictive maintenancedata-drivenclassificationsmart power applications