A Novel Hybrid Intelligent Method for Fault Diagnosis of the Complex System

In allusion to the low correctness and efficiency of fault diagnosis for the complex industrial system, rough set theory, particle swarm optimization and back propagation (BP) neural network are introduced to propose a hybrid intelligent fault diagnosis(RPBPNN) method in this paper. In the proposed RPBPNN method, rough set theory as a new mathematical tool is used to process inexact and uncertain knowledge in order to obtain the minimum fault characteristic set for simplifying the structure and improving learning efficiency of BPNN. The particle swarm optimization (PSO) algorithm with the global optimization ability is directly used to train the weights of BP neural network in order to establish the optimized BP neural network model. Then the minimum fault characteristic set is used to train the optimized BP neural network model in order to obtain the optimal BP neural network model for realizing the fault diagnosis. Finally, the proposed RPBPNN method is applied to an actual application case for verifying the effectiveness. The experimental results show that PSO algorithm can search for the optimal values of BPNN parameters and the proposed RPBPNN method can accurately eliminate false and improve the diagnostic accuracy. So the proposed RPBPNN method takes on better generalization performance and prediction accuracy in the real industrial application system.