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Open set compound fault recognition method for nuclear power plant based on label mask weighted prototype learning

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  • Zhou, Shiqi
  • Lin, Meng
  • Huang, Shilong
  • Xiao, Kai

Abstract

Most of the existing data-driven methods for diagnosing faults in nuclear power plants (NPP) concentrate on addressing single fault problems under the closed set hypothesis. However, the practical application of these methods is severely limited by unknown faults and compound faults caused by the complex operation characteristics of NPP. In order to extend the NPP fault diagnosis model to the Open Set Compound Fault Recognition (OSCFR), a method for OSCFR based on residual capsule network and label mask weighted prototype learning (Mask-CCPN) is proposed. In this method, the residual capsule network is constructed to achieve the decoupled classification of compound faults. Additionally, the label mask weighted method is used to enhance the existing prototype learning, which using the prototype information from single faults to identify the compound faults. Numerical experiments on complex NPP simulation data demonstrate that the proposed method can effectively solve the OSCFR of NPP without requiring additional storage space. The test results under a single fault data set are superior to the state-of-the-art method. In addition, this study confirms that the uniform distribution hypothesis is more suitable for the decision-making process of prototype learning, while the Gaussian distribution hypothesis and Weibull distribution hypothesis have poor results. Overall, the proposed method is promising to expand the application range of NPP fault diagnosis to various types of faults.

Suggested Citation

  • Zhou, Shiqi & Lin, Meng & Huang, Shilong & Xiao, Kai, 2024. "Open set compound fault recognition method for nuclear power plant based on label mask weighted prototype learning," Applied Energy, Elsevier, vol. 369(C).
    • Handle: RePEc:eee:appene:v:369:y:2024:i:c:s0306261924009863
    DOI: 10.1016/j.apenergy.2024.123603
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