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Related Experiment Video

Updated: Jul 8, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

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

Summary

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.

Related Experiment Videos

Last Updated: Jul 8, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

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.