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Two-Step Neural-Network-Based Fault Isolation for Stochastic Systems

Author

Listed:
  • Liping Yin

    (Shoool of Ationautom, Nanjing University of Information Science & Techonlogy, Nanjing 210044, China
    Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing 210044, China)

  • Jianguo Liu

    (Shoool of Ationautom, Nanjing University of Information Science & Techonlogy, Nanjing 210044, China)

  • Hongquan Qu

    (School of Information, North China University of Technology, Langfang 065000, China)

  • Tao Li

    (Shoool of Ationautom, Nanjing University of Information Science & Techonlogy, Nanjing 210044, China
    Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing 210044, China)

Abstract

This paper studies a fault isolation method for an optical fiber vibration source detection and early warning system. We regard the vibration sources in the system as faults and then detect and isolate the faults of the system based on a two-step neural network. Firstly, the square root B-spline expansion method is used to approximate the output probability density functions. Secondly, the nonlinear weight dynamic model is established through a dynamic neural network. Thirdly, the nonlinear filter and residual generator are constructed to estimate the weight, analyze the residual, and estimate the threshold, so as to detect, diagnose, and isolate the faults. The feasibility criterion of fault detection and isolation is given by using some linear matrix inequalities, and the stability of the estimation error system is proven according to the Lyapunov theorem. Finally, simulation experiments based on a optical fiber vibration source system are given to verify the effectiveness of this method.

Suggested Citation

  • Liping Yin & Jianguo Liu & Hongquan Qu & Tao Li, 2022. "Two-Step Neural-Network-Based Fault Isolation for Stochastic Systems," Mathematics, MDPI, vol. 10(22), pages 1-12, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4261-:d:972575
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