基于改进粒子群优化算法的电机故障诊断研究

针对电机转子故障,利用神经网络方法进行故障诊断研究。将基本粒子群优化(PSO)算法进行改进,并用其训练反向传播(BP)神经网络,对电机转子进行故障诊断。选用电机转子振动频谱分量作为神经网络的训练样本,将故障信息数据作为输入量代入已训练好的神经网络,通过输出结果即可诊断故障类型。仿真结果表明,基于改进PSO算法的BP神经网络可以有效地识别电机常见故障,具有较快的收敛速度和较高的诊断精度。

Research for Motor Failure Diagnosis Based on Particle Swarm Optimization Algorithm

For the failures of the motor,the method of failures diagnosis is studied by using the neural network. Making the improvement to the basic Particle Swarm Optimization(PSO) algorithm,and then it is used in training the Back Propagation(BP) neural network to carry out failure diagnosis for the motor rotor.By withdrawing motor rotor' s vibration frequency spectrum component as the neural network' s training sample,inputs the failure information data to the neural network trained well already,then diagnoses the...