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Fault Identification for a Closed-Loop Control System Based on an Improved Deep Neural Network.

Bowen Sun1, Jiongqi Wang2, Zhangming He3,4

  • 1College of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410073, China. sunbowen2017@sina.com.

Sensors (Basel, Switzerland)
|May 11, 2019
PubMed
Summary

Identifying faults in closed-loop control systems is challenging due to fault propagation. This study introduces a novel deep neural network method that enhances fault identification performance in these complex systems.

Keywords:
closed-loop control systemdeep neural networkfault diagnosisidentification performancesliding window

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Area of Science:

  • Control Systems Engineering
  • Fault Diagnosis and Prognostics
  • Artificial Intelligence in Engineering

Background:

  • Closed-loop control systems exhibit robustness but complicate fault identification due to fault propagation, which attenuates fault signals.
  • Traditional fault identification methods are often inadequate for closed-loop systems, necessitating new approaches.
  • The subtle nature of faults in closed-loop systems makes their detection and characterization difficult.

Purpose of the Study:

  • To develop a novel fault identification method specifically for closed-loop control systems.
  • To address the challenges posed by fault propagation and signal attenuation in these systems.
  • To improve the accuracy and effectiveness of fault identification in complex control environments.

Main Methods:

  • Theoretical derivation of the fault propagation mechanism within closed-loop control systems.
  • Application of deep neural networks (DNNs) to discern subtle fault characteristics from system data.
  • Utilization of a sliding window technique to enhance the fault-to-noise ratio and characteristic differences.

Main Results:

  • The proposed deep neural network method demonstrated superior feasibility and effectiveness in fault identification.
  • Simulations on a numerical model, the Tennessee industrial system, and a satellite attitude control system validated the approach.
  • The method significantly outperformed traditional data-driven techniques like distance-based and angle-based identification.

Conclusions:

  • The developed deep neural network-based method offers a promising solution for fault identification in closed-loop control systems.
  • The approach effectively overcomes the limitations imposed by fault propagation and signal masking.
  • This advancement contributes to more reliable and robust operation of critical closed-loop control systems.