Predictive maintenance of VRLA batteries in UPS towards reliable data centers

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%.