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An asymmetrical graph Siamese network for one-classanomaly detection of engine equipment with multi-source fusion

Author

Listed:
  • Wang, Weicheng
  • Chen, Jinglong
  • Zhang, Tianci
  • Liu, Zijun
  • Wang, Jun
  • Zhang, Xinwei
  • He, Shuilong

Abstract

For the reliability and safety of engine equipment, real-time anomaly detection through monitoring signals from multi-source sensors is essential. However, signal coupling caused by complicated interactions between numerous components raises a challenge. Additionally, due to the extreme operating environment and severe malfunction result, the failure data is difficult to collect or simulate, leading to the lack of anomaly samples. This paper proposed an asymmetrical graph Siamese network (AGSN) for one-class anomaly detection with multi-source fusion. The network consists of two weights-shared graph encoders and an extra remapping block which prevents the model from collapsing when one-class training. Firstly, AGSN adaptively constructs the graph structure based on sensor signals to model the components of systems and fuse multi-source signals into graph data. Secondly, graph data of normal samples are input into the AGSN for graph contrastive learning, enabling the graph encoders to completely cluster normal samples in the feature space. Thus, anomalous samples can be distinguished from normal samples when anomaly detection. The AGSN is evaluated on two datasets of liquid rocket engine (LRE) multi-sensor signals and compared with baseline approaches. The experimental results demonstrate that the proposed model is efficient, lightweight, and reliable, outperforming existing methods.

Suggested Citation

  • Wang, Weicheng & Chen, Jinglong & Zhang, Tianci & Liu, Zijun & Wang, Jun & Zhang, Xinwei & He, Shuilong, 2023. "An asymmetrical graph Siamese network for one-classanomaly detection of engine equipment with multi-source fusion," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    • Handle: RePEc:eee:reensy:v:235:y:2023:i:c:s0951832023001734
    DOI: 10.1016/j.ress.2023.109258
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    References listed on IDEAS

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    1. Chen, Zhen & Zhou, Di & Zio, Enrico & Xia, Tangbin & Pan, Ershun, 2023. "Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    2. Brian C Ross, 2014. "Mutual Information between Discrete and Continuous Data Sets," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-5, February.
    3. Li, Fudong & Chen, Jinglong & Liu, Zijun & Lv, Haixin & Wang, Jun & Yuan, Junshe & Xiao, Wenrong, 2022. "A soft-target difference scaling network via relational knowledge distillation for fault detection of liquid rocket engine under multi-source trouble-free samples," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
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    5. Shao, Kaixuan & He, Yigang & Xing, Zhikai & Du, Bolun, 2023. "Detecting wind turbine anomalies using nonlinear dynamic parameters-assisted machine learning with normal samples," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    6. Pan, Tongyang & Chen, Jinglong & Ye, Zhisheng & Li, Aimin, 2022. "A multi-head attention network with adaptive meta-transfer learning for RUL prediction of rocket engines," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    7. Chen, Hansi & Liu, Hang & Chu, Xuening & Liu, Qingxiu & Xue, Deyi, 2021. "Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network," Renewable Energy, Elsevier, vol. 172(C), pages 829-840.
    8. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
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    Cited by:

    1. Zhang, Xinwei & Feng, Yong & Chen, Jinglong & Liu, Zijun & Wang, Jun & Huang, Hong, 2024. "Knowledge distillation-optimized two-stage anomaly detection for liquid rocket engine with missing multimodal data," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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