Research on equipment fault diagnosis method based on random stochastic adaptive particle swarm optimization

摘要

The traditional fault diagnosis method of industrial equipment has low accuracy and poor applicability. This paper proposes a equipment fault diagnosis method based on random stochastic adaptive particle swarm optimization(RSAPSO). The entire model is validated by using the data of healthy bearings collected by Case Western Reserve University. Different gradient descent algorithms and standard particle swarm optimization(PSO) algorithms in a back propagation(BP) network are compared experimentally. The results show that the RSAPSO algorithm has a higher accuracy of weight threshold updating than the gradient descent algorithm and does not easily fall into a local optimum. Compared with PSO, it has a faster optimization speed and higher accuracy. Finally, the RSAPSO algorithm is validated with the data of bearings collected from the laboratory rotating machinery test bench and motor data collected from the tower reflux pump. The average recognition rate of the four kinds of bearing data constructed is 97.5%, and the average recognition rate of the two kinds of motor data reaches 100%, which prove the universality of the method.