Particle swarm optimization algorithm with adaptive velocity and its application to fault diagnosis

This paper introduces a particle swarm optimization algorithm with adaptive velocity (VPSO), in which a moving maximum limited velocity is set in original particle swarm optimization (PSO) algorithm to improve the performance of the PSO. The test results by neural network show that this algorithm is better than original PSO in convergent speed and accuracy, and its parameters selection is flexible and is easily realized. The modified algorithm has been applied to fault diagnosis system of neural network for an experimental gearbox, and compared with the PSO and BP algorithm. The conclusion is that VPSO applying to fault diagnosis system not only has higher discrimination for gearbox faults, but also greatly improves the accuracy and efficiency of fault diagnosis.