速度自适应粒子群优化算法在故障诊断中的应用

在原始粒子群优化算法(PSO)中设置动态最大限制速度基础上,提出一种速度自适应粒子群优化算法。经过神经网络的测试表明,该算法在收敛速度和精度上都优于原始算法,并且参数选取灵活,容易实现。将改进算法应用于实验室变速箱的神经网络故障诊断系统中,并与PSO和BP算法进行了比较,得出该算法不仅对变速箱故障的识别准确率比较高,而且故障诊断的精度和效率也较高。

Application of Particle Swarm Optimization Algorithm With Adaptive Velocity to Fault Diagnosis

Particle swarm optimization algorithm with adaptive velocity(VPSO) has been proposed,based on the setting of moving maximum limiting velocity in original particle swarm optimization(PSO) algorithm.The testing results by neural network show that this algorithm is better than original PSO in convergent speed and accuracy,and its parameter selection is flexible and easily realized.The modified algorithm has been applied to fault diagnosis system of neural network for an experimental gearbox,and compared with 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 improve the accuracy and efficiency of fault diagnosis.