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Integrating Sensor Embeddings with Variant Transformer Graph Networks for Enhanced Anomaly Detection in Multi-Source Data

Author

Listed:
  • Fanjie Meng

    (School of Aerospace Science and Technology, Xidian University, Xi’an 710071, China)

  • Liwei Ma

    (School of Mechanical Engineering, Xi’an Jiao Tong University, Xi’an 710049, China)

  • Yixin Chen

    (Key Laboratory of Expressway Construction Machinery of Shaanxi Province, Chang’an University, Xi’an 710064, China)

  • Wangpeng He

    (School of Aerospace Science and Technology, Xidian University, Xi’an 710071, China)

  • Zhaoqiang Wang

    (High-Tech Institute of Xi’an, Xi’an 710025, China)

  • Yu Wang

    (School of Mechanical Engineering, Xi’an Jiao Tong University, Xi’an 710049, China)

Abstract

With the rapid development of sensor technology, the anomaly detection of multi-source time series data becomes more and more important. Traditional anomaly detection methods deal with the temporal and spatial information in the data independently, and fail to make full use of the potential of spatio-temporal information. To address this issue, this paper proposes a novel integration method that combines sensor embeddings and temporal representation networks, effectively exploiting spatio-temporal dynamics. In addition, the graph neural network is introduced to skillfully simulate the complexity of multi-source heterogeneous data. By applying a dual loss function—consisting of a reconstruction loss and a prediction loss—we further improve the accuracy of anomaly detection. This strategy not only promotes the ability to learn normal behavior patterns from historical data, but also significantly improves the predictive ability of the model, making anomaly detection more accurate. Experimental results on four multi-source sensor datasets show that our proposed method performs better than the existing models. In addition, our approach enhances the ability to interpret anomaly detection by analyzing the sensors associated with the detected anomalies.

Suggested Citation

  • Fanjie Meng & Liwei Ma & Yixin Chen & Wangpeng He & Zhaoqiang Wang & Yu Wang, 2024. "Integrating Sensor Embeddings with Variant Transformer Graph Networks for Enhanced Anomaly Detection in Multi-Source Data," Mathematics, MDPI, vol. 12(17), pages 1-14, August.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2612-:d:1462830
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