Exploiting Particle Swarm Optimization in Multiple Faults Fuzzy Detection
This work addresses fault diagnosis in hydraulic systems, but it is incremental as it applies PSO to an existing fuzzy detection framework.
The paper tackles the problem of online multiple fault detection by proposing an approach that uses Particle Swarm Optimization (PSO) to optimize membership functions in fuzzy detection, applied to a three-tank hydraulic system simulation, with results compared to Genetic Algorithms (GA).
In this paper an on-line multiple faults detection approach is first of all proposed. For efficiency, an optimal design of membership functions is required. Thus, the proposed approach is improved using Particle Swarm Optimization (PSO) technique. The inputs of the proposed approaches 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. A comparison between the results obtained by using PSO and those given by the use of Genetic Algorithms (GA) is finally made. The experiments focus on a simulation of the three-tank hydraulic system, a benchmark in the diagnosis domain.