Predictive Maintenance of VRLA Batteries in UPS towards Reliable Data Centers. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Tang, Jing-Xian; Du, Jin-Hong; Lin, Yiting; Jia, Qing-Shan
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 13607
• Page End: 13612
• Publication Date: 2020
• Subjects: Predictive maintenance -- data-driven -- classification -- smart power applications; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.854
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: The 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%.