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Fault Propagation Inference Based on a Graph Neural Network for Steam Turbine Systems

Author

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  • Yi-Jing Zhang

    (Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
    These authors contributed equally to this work.)

  • Li-Sheng Hu

    (Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
    These authors contributed equally to this work.)

Abstract

A fault propagates along physical paths until it reaches the boundary of the equipment or system, which shows as a functional failure. Hence, inferring the fault propagation helps to ensure the normal operation of the industrial system. To infer the fault propagation in the steam turbine system, a graph model is developed. Firstly, a process graph topology is constructed according to the system mechanism, whose nodes and edges represent the equipment and mutual relationships. Meanwhile, a fault graph topology is built, in which nodes indicate potential faults and edges are inferred propagation paths. Then, the representations of fault nodes are realized through a graph neural network. Lastly, link prediction methods based on nodes’ representations are conducted, along with the paths inference results. Consequently, the accuracy of fault propagation inference for the steam turbine system is over 86%.

Suggested Citation

  • Yi-Jing Zhang & Li-Sheng Hu, 2021. "Fault Propagation Inference Based on a Graph Neural Network for Steam Turbine Systems," Energies, MDPI, vol. 14(2), pages 1-13, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:309-:d:476747
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    References listed on IDEAS

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    Cited by:

    1. Salman Khalid & Jinwoo Song & Izaz Raouf & Heung Soo Kim, 2023. "Advances in Fault Detection and Diagnosis for Thermal Power Plants: A Review of Intelligent Techniques," Mathematics, MDPI, vol. 11(8), pages 1-28, April.

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