摘要
针对支持向量机(SVM)对处理大样本数据和多分类问题以及核函数选择的局限性,提出LMD支持向量机电机轴承故障诊断方法。首先应用局域均值分解(LMD)算法对信号进行自适应分解,得到一系列PF分量,并利用相关分析剔除虚假分量,提取真实PF分量能量组成特征向量;其次应用新的核函数对SVM进行改进,实现自适应的训练,并针对大样本数据和多分类问题采用‘一对多’的方法;最后以特征向量作为改进SVM的训练样本和测试样本,对电机轴承故障信息进行训练,预测。实验验证,该方法能有效的对电机轴承故障进行自适应的诊断。
Aiming at the limitation of the support vector machine(SVM)to deal with the large sample data and the multi classification problem and the selection of kernel function,the fault diagnosis method of motor bearing based on LMD and support vector machine is proposed.Firstly,the local mean decomposition(LMD)algorithm is used to adaptively decompose the signal to get a series of PF components,and the correlation analysis is used to eliminate false components.Then,the energy feature vector is formed by extracting energy of the real PF component.Secondly,the new kernel function is used to improve the SVM to complete the adaptive training,and the“one to many”method is used to solve the large sample data and multi classification problem.Finally,the energy feature vector is used as the training sample and test sample of SVM,and the fault information of motor bearing is trained and predicted.Experimental results show that the proposed method can effectively diagnose the fault of motor bearing.
作者
尹召杰
许同乐
郑店坤
YIN Zhao-jie;XU Tong-le;ZHENG Dian-kun(School of Mechanical Engineering,Shandong University of Technology,Zibo 255049,China)
出处
《哈尔滨理工大学学报》
CAS
北大核心
2018年第5期35-39,共5页
Journal of Harbin University of Science and Technology
基金
山东省自然科学基金(ZR2013FM005)
淄博市科技发展计划项目(2015kj040008)
关键词
局域均值分解
支持向量机
故障诊断
电机轴承故障
local mean decomposition
support vector machine
fault diagnosis
bearing fault of motor
作者简介
尹召杰(1992—),男,硕士研究生;许同乐(1965—),男,教授,硕士研究生导师,E-mail:xutongle@163.com.;郑店坤(1990—),男,硕士研究生.
