Application of PSO-based rough set theory and neural network to aeroengine fault diagnosis

A new method based on neighborhood rough set model and neural networks(NN)integrated with particle swarm optimization(PSO) algorithm was presented in this paper for fault diagnosis of aeroengine.Firstly,using the algorithm of attribute reducing based on neighborhood rough set model,we deleted the unnecessary attributes from the decision table.Secondly,the PSO was used to train the weights and the thresholds of NN instead of BP algorithm.Therefore,The NN trained by PSO was applied to aeroengine fault diagnosis.The simulation results indicate that the method has shortened the training time and increased the diagnosis accuracy.