Combining fuzzy inference, cultural algorithm and causal reasoning to diagnose faults in complex systems

To ensure complex systems reliability and to extent their life cycle, it is crucial to properly and timely detect and localize the causes of eventual faults. In this context, this paper describes a new intelligent approach to diagnose (single and multiple) faults in complex systems. This approach is based on the combination of several intelligent techniques. This approach begins by detecting the presence of faults using a fuzzy inference system. The parameters of the fuzzy detector are optimized using a population-based strategy: the Cultural Algorithm. The inputs of the proposed approach are residuals representing the numerical evaluation of Analytical Redundancy Relations. These residuals are generated due to the use of bond graph modeling. The results of the fuzzy detection modules are displayed as a colored causal graph. If at least one fault is detected, a localization step is launched. This second step is based on causal graph reasoning. The experiments focus on a simulation of the three-tank hydraulic system, a benchmark in the diagnosis domain.