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Max-product algorithms for the generalized multiple-fault diagnosis problem

Tung Le1, Christoforos N Hadjicostis

  • 1Department of Electrical and Computer Engineering, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. tungle1@uiuc.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 9, 2008
PubMed

Insights

The max-product algorithm (MPA) and sequential MPA (SMPA) effectively diagnose generalized multiple faults, especially in sparse systems. SMPA shows improved performance in complex systems with cycles.

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Systems Engineering

Background:

  • The generalized multiple-fault diagnosis (GMFD) problem involves identifying faults in systems with unreliable components and alarms.
  • Existing diagnostic methods face challenges in complex systems with interconnected components and potential cycles.

Purpose of the Study:

  • To apply and evaluate the max-product algorithm (MPA) and a novel sequential MPA (SMPA) for GMFD.
  • To analyze algorithm performance on bipartite diagnosis graphs (BDGs), including acyclic and cyclic cases.

Main Methods:

  • Developed and applied MPA and SMPA, which are local message-passing algorithms.
  • Utilized bipartite diagnosis graphs (BDGs) to model the GMFD problem.
  • Conducted simulations and analytical assessments for various graph structures and alarm patterns.

Main Results:

  • MPA and SMPA converge to the maximum a posteriori probability (MAP) solution for acyclic BDGs.
  • Simulations indicate strong performance for both MPA and SMPA in general systems, with SMPA outperforming MPA in systems with cycles.
  • Analytical bounds on erroneous diagnosis probability were derived for general graphs.

Conclusions:

  • MPA and SMPA offer efficient solutions for GMFD with reasonable computational complexity, particularly for sparse diagnosis graphs.
  • SMPA demonstrates enhanced performance over MPA in more complex, cyclic system models.
  • The algorithms provide a robust approach to fault diagnosis in unreliable systems.

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