This is an interim version of our Electronic Legal Deposit Catalogue-eJournals and eBooks while we continue to recover from a cyber-attack.
Predictive Maintenance of VRLA Batteries in UPS towards Reliable Data Centers⁎This work is supported in part by the National Natural Science Foundation of China (Grant No. 61673229), the Major Project of the Ministry of Science and Technology of China (Grant No. 2018AAA0101600), the National Key Research and Development Program of China (Grant No. 2016YFB0901900), the 111 International Collaboration Project of China (Grant No. BP2018006) and Tsinghua-Tencent Cooperation Research Project. 'Contributed equally. Issue 2 (2020)
Record Type:
Journal Article
Title:
Predictive Maintenance of VRLA Batteries in UPS towards Reliable Data Centers⁎This work is supported in part by the National Natural Science Foundation of China (Grant No. 61673229), the Major Project of the Ministry of Science and Technology of China (Grant No. 2018AAA0101600), the National Key Research and Development Program of China (Grant No. 2016YFB0901900), the 111 International Collaboration Project of China (Grant No. BP2018006) and Tsinghua-Tencent Cooperation Research Project. 'Contributed equally. Issue 2 (2020)
Main Title:
Predictive Maintenance of VRLA Batteries in UPS towards Reliable Data Centers⁎This work is supported in part by the National Natural Science Foundation of China (Grant No. 61673229), the Major Project of the Ministry of Science and Technology of China (Grant No. 2018AAA0101600), the National Key Research and Development Program of China (Grant No. 2016YFB0901900), the 111 International Collaboration Project of China (Grant No. BP2018006) and Tsinghua-Tencent Cooperation Research Project. 'Contributed equally.
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%.