Fault diagnosis based on WNNs with parameters optimization by immune evolutionary Particle Swarm Algorithm

The immune evolutionary mechanism of artificial immune system is used into Particle Swarm Optimization(IEPSO). A new training algorithm in wavelet neural networks(WNNs) based on IEPSO is presented, it can avoid early ripe of PSO and traditional BP algorithm. In the course of optimizing the parameters of WNNs, new algorithm use the immune evolutionary principle to improve the process of PSO, it determines the probability of their choice based on the size of fitness and concentration in antibodies, and dynamically adjusted crossover probability and mutation probability by use of fitness function. With the parameters optimized by IEPSO, the convergence performance of the WNNs is improved. The fault diagnosis of progressing cavity pumps well shows that the WNNs optimized by IEPSO can give higher recognition accuracy than the normal WNNs.