矢功率谱与蚁群神经网络结合在机械故障诊断中的应用研究  

Vector power spectrum and ant colony optimization of neural network applied to rotating equipment fault diagnosis

作　　者：杨春燕[1] 云康[1] 杜文辽[1] 

机构地区：[1]郑州轻工业学院机电工程学院,郑州450002

出　　处：《现代制造工程》2013年第6期121-125,共5页Modern Manufacturing Engineering

基　　金：国家自然科学基金青年科学基金项目(51205371);河南省科技攻关计划项目(122102210122)

摘　　要：针对传统功率谱信号源不足以及BP神经网络收敛速度慢且容易陷入局部极小等问题,提出矢功率谱和蚁群神经网络相结合的故障诊断方法,该方法是:提取矢功率谱的8个频段能量特征,并输入到蚁群神经网络分类器进行故障识别,通过实际训练结果和实验结果对比可知,蚁群神经网络能有效地提高收敛速度,网络迭代次数明显改善,故障识别率提高,将蚁群神经网络应用于机械故障诊断是有效的。The signal source of traditional power spectrum is insufficient, the convergence speed of BP neural network is slow and may inevitably meet local minimal problems. According to these problems, a new fault diagnosis approach is proposed, this ap-proach is that the vector power spectrum is used as eigenvectors, the ant colony neural network as a classifier. The experiment re-suits shows that the ant colony neural network can effectively improve the convergence speed and fault identification. So the pro-posed approach applied to machinery fault diagnosis is very effective.

关 键 词：矢功率谱 蚁群算法 BP神经网络 故障诊断 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术]