Rolling Bearing Fault Diagnosis Based on the Hybrid Algorithm of Particle Swarm Optimization with Neighborhood Operator

In order to improve the accuracy of rolling bearing fault diagnosis, a hybrid algorithm of particle swarm optimization with neighborhood operator is applied. According to the fault feature vectors, PSO with neighborhood operator is applied to optimize the weight of BP neural network, then the fault diagnosis is accomplished via the optimized neural network. The simulation results show that this method has better classification results for rolling bearing fault diagnosis and has a certain practicality.