量子粒子群优化算法在异步电机转子机械故障诊断中的应用  

Application of Quantum Particle Swarm Optimization Algorithm in Fault Diagnosis for Asynchronous Motor

作　　者：段志梅[1] 程加堂[1] 

机构地区：[1]红河学院工学院,云南蒙自661199

出　　处：《电机与控制应用》2016年第3期89-92,共4页Electric machines & control application

基　　金：云南省教育厅科研基金资助项目(2014Y458)

摘　　要：为了提高异步电机转子机械故障诊断的准确性,引入了一种基于量子粒子群优化BP神经网络(QPSO-BP)的诊断方法。以转子机械故障为研究对象,利用量子粒子群算法(QPSO)来优化BP网络的权、阈值参数,并最终实现了转子机械故障诊断模型的构建。仿真实例表明,基于QPSO-BP的故障诊断方法,能有效识别异步电机的转子机械故障类型,较粒子群优化BP网络算法(PSO-BP)具有更高的诊断准确率。In order to improve the accuracy of asynchronous motor fault diagnosis,a method of quantum particle swarm optimized BP neural network（ QPSO-BP） was proposed. Taking the rotor fault for the study,the quantum particle swarm optimization（ QPSO） algorithm was used to optimize the weight and threshold parameters of BP neural network,ultimately the asynchronous motor fault diagnosis model was established. The simulation showed that QPSOBP algorithm can effectively identify the type of fault asynchronous motor,and had a higher diagnostic accuracy compared with particle swarm optimized BP neural network（ PSO-BP）.

关 键 词：量子粒子群优化算法 神经网络 异步电机 故障诊断 

分 类 号：TM307[电气工程—电机]