Multi-Tasks Joint Network for Anomaly Diagnosis and Inconsistent Identification of VRLA Battery in Large Data Center

Abstract

Valve-regulated lead–acid (VRLA) battery module is one of the important components of the uninterruptible power supply system in a large Internet data center. Battery health monitoring and anomaly diagnosis in time is significant to ensure the safe operation of a data center. In this article, a multitask joint network (MTJNet) is proposed to perform anomaly diagnosis and inconsistency identification simultaneously. First, an unsupervised learning-based encoder-decoder structure is proposed in MTJNet for battery inconsistency identification, where only health data are required for training. Second, the other branch with a classifier is further constructed to recognize the anomaly of the battery module. Third, a multitask joint training method is used to update the parameters of the two task models in MTJNet. The reconstruction error obtained by inconsistency identification task is fed into the anomaly diagnosis task. The predicted pseudo labels by the anomaly diagnosis task are feedback to inconsistency identification task to guide the encoder-decoder to provide the discriminate features. Finally, the effectiveness of MTJNet is verified on VRLA battery modules in a large data center. The experimental results illustrate that MTJNet is a good tool for anomaly diagnosis and inconsistency identification of VRLA battery modules.