Fault Diagnosis Research by Rough Set Theory and the PSO-BP Neural Network
Maoyun Guo · 2011
For the imperfections of BP network fault diagnosis model,including the complexity of the network structure,the long time of training,and the low precision,this article introduced rough set(RS),particle swarm optimization(PSO) and genetic algorithm(GA) into the diesel engine fault diagnosis,then proposed a new algorithm that is based on rough set theory and the improved BP neural network.The algorithm uses self-organization mapping net(SOM) to discretize the continuous attributes,rough set theory to make a reduction on the properties for characteristic parameters,and the particle swarm optimization(PSO) to optimize the BP network structure,so that it can shorten training time and improve the accuracy of fault diagnosis effectively.Finally,the result of the diesel engine's diagnosis proves the feasibi-lity,rapidity,veracity of the algorithm.