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Model-based fault diagnosis in continuous dynamic systems.

C H Lo1, Y K Wong, A B Rad

  • 1Dept. of Electrical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. eechlo@polyu.edu.hk

ISA Transactions
|July 27, 2004
PubMed
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This study introduces a new fault detection method using qualitative bond graphs (QBG) and genetic algorithms (GA). This approach simplifies fault diagnosis in mechatronic systems by analyzing qualitative equations, reducing reliance on complex quantitative models.

Area of Science:

  • Engineering
  • Mechatronics
  • Control Systems

Background:

  • Traditional fault detection relies on complex and costly quantitative models.
  • A need exists for more accessible and efficient fault diagnosis methods in engineering systems.

Purpose of the Study:

  • To develop a model-based fault detection and diagnosis algorithm using qualitative bond graph (QBG) reasoning.
  • To address the limitations of traditional quantitative modeling in fault detection.

Main Methods:

  • Utilized qualitative bond graph (QBG) formalism to generate qualitative equations for system modeling.
  • Implemented continuous system monitoring through input-output qualitative equations.
  • Employed genetic algorithms (GA) for searching and identifying faulty components within the qualitative model.

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Main Results:

  • The proposed QBG-based method enables continuous monitoring and fault diagnosis.
  • Genetic algorithms effectively identified potential faulty components in the system.
  • The algorithm demonstrated performance on a laboratory-scale servo-tank liquid process rig.

Conclusions:

  • Qualitative bond graph reasoning offers a unified and efficient approach to modeling engineering systems, particularly mechatronic systems.
  • The integration of QBG and genetic algorithms provides a viable and effective model-based fault detection and diagnosis solution.
  • This method reduces the dependency on difficult-to-obtain quantitative models for fault diagnosis.