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Multi-source self-supervised domain adaptation network for VRLA battery anomaly detection of data center under non-ideal conditions

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  • Miao, Mengqi
  • Yang, Pu
  • Yue, Shang
  • Zhou, Ruixu
  • Yu, Jianbo

Abstract

As the key component of uninterruptible power supply, the anomaly detection of valve-regulated lead-acid (VRLA) batteries is the reliable guarantee for the operation of internet data centers. Although deep neural networks (DNNs) have been investigated in battery anomaly detection, the key problem of feature learning under non-ideal conditions, i.e., data scarcity and class-imbalance, is not addressed very well. In this article, a novel DNN called multi-source self-supervised domain adaptation network (MSSSDAN) is proposed for VRLA battery anomaly detection of data center under non-ideal conditions. Firstly, multi-dimensional collaborative self-supervised learning (MDCSSL) is proposed to address the class-imbalanced problem by performing auxiliary feature learning task considering the temporal dependency of different channels. Secondly, a novel transfer learning (TL) framework, multi-source adaptive transfer learning (MSATL) is developed to solve the domain shift problem by adaptively minimizing the distribution discrepancy of different domains. Finally, the outperformance of MSSSDAN is verified on the VRLA battery dataset collected from Tencent data center. The testing results demonstrate that MSSSDAN can effectively solve the problem of battery anomaly detection under non-ideal conditions.

Suggested Citation

  • Miao, Mengqi & Yang, Pu & Yue, Shang & Zhou, Ruixu & Yu, Jianbo, 2024. "Multi-source self-supervised domain adaptation network for VRLA battery anomaly detection of data center under non-ideal conditions," Energy, Elsevier, vol. 299(C).
    • Handle: RePEc:eee:energy:v:299:y:2024:i:c:s0360544224011654
    DOI: 10.1016/j.energy.2024.131392
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    References listed on IDEAS

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    Cited by:

    1. Miao, Mengqi & Wang, Yun & Yu, Jianbo, 2024. "Temporal self-supervised domain adaptation network for machinery fault diagnosis under multiple non-ideal conditions," Reliability Engineering and System Safety, Elsevier, vol. 251(C).

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