Diagnosing Multiple Faults in Dynamic Hybrid Systems

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 182)

Abstract

Due to their quite complex nature, Dynamic Hybrid Systems represent a constant challenge for their diagnosing. In this context, this paper proposes a general multiple faults model-based diagnosis methodology for hybrid dynamic systems characterized by slow discernable discrete modes. Each discrete mode has a continuous behavior. The considered systems are modeled using hybrid bond graph which allows the generating of residuals (Analytical Redundancy Relations) for each discrete mode. The evaluation of such residuals (detection faults step) extends previous works and is based on the combination of adaptive thresholdings and fuzzy logic reasoning. The performance of fuzzy logic detection is generally linked to its membership functions parameters.Thus, we rely on Particle Swarm Optimization (PSO) to get optimal fuzzy partition parameters. The results of the diagnosis module are finally displayed as a colored causal graph indicating the status of each system variable in each discrete mode. To make evidence of the effectiveness of the proposed solution, we rely on a diagnosis benchmark: The three-tank system.

Keywords

Particle Swarm Optimization Fault Detection Multiple Fault Discrete Mode Causal Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.SOIE LaboratoryNational School of Computer Sciences-TunisiaTunisTunisia

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