A variant of the particle swarm optimization for the improvement of fault diagnosis in industrial systems via faults estimation
This paper proposes an approach for Fault Diagnosis and Isolation (FDI) on industrial systems via faults estimation. FDI is presented as an optimization problem and it is solved with Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms. Also, is presented a study of the influence of some parameters from PSO and ACO in the desirable characteristics of FDI, i.e. robustness and sensitivity. As a consequence, the Particle Swarm Optimization with Memory (PSO-M) algorithm, a new variant of PSO was developed. PSO-M has the objective of reducing the number of iterations/generations that PSO needs to execute in order to provide a reasonable quality diagnosis. The proposed approach is tested using simulated data from a DC Motor benchmark. The results and analysis indicate the suitability of the approach as well as the PSO-M algorithm.
particle swarm optimization fault diagnosis industrial systems ant colony optimization robust diagnosis sensitive diagnosis