Densely-Connected Contrastive Observer for Fault Diagnosis of VRLA Battery in Large Data Center Based on Semisupervised Few-Shot Learning

Valve-regulated lead-acid (VRLA) batteries are of paramount importance in ensuring the safety and stability of large intelligent data centers (IDCs). The health management of VRLA battery suffers from the limitation of sparse charge-discharge data and nonideal samples in the high-voltage direct current system of IDC. In this article, a novel semisupervised few-shot model, i.e., densely-connected contrastive observer (DCober), is proposed for feature self-extraction of multisource signals and few-shot diagnosis of VRLA batteries. First, a view-augmented method is proposed to implement image augmentation for multichannel temporal images, which improves the feature self-extraction performance. Second, self-supervised contrastive learning is used in DCober to implement feature representation learning on massive nonlabeled data in real industrial scenarios. Finally, a similarity-based few-shot finetuning method is proposed for finetuning enhancement on the extension of limited labeled data, which improves the fault recognition performance of DCober in few-shot scenarios. The effectiveness of DCober is validated through a real industrial application of VRLA battery in Tencent T-block IDC. The testing results show that DCober outperforms other state-of-the-art methods and obtains a recognition accuracy of 95.63% in 1-shot data scenario.