摘要
针对故障诊断中各类数据样本分布不均衡时诊断效果较差的问题,在支持向量机方法的基础上引入了支持向量描述方法,并对支持向量描述方法向多元判决扩展的方法,以及各种扩展方法的局限性进行了研究。对现有支持向量描述方法的扩展方法进行了改进,提出了一种利用K相邻概率加权的多元判决支持向量数据描述方法;通过使用集合经验模态分解原信号,并计算了各本征模函数的信息量作为特征,利用第三方实验数据,对K相邻概率支持向量数据描述方法在各种故障类别的识别准确率进行了测试。研究结果表明:该方法可以有效地识别滚动轴承故障发生时的位置和严重程度;同时,通过与其他分类方法对比的方式,证明了该方法的优越性。
Aiming at the problem of poor diagnosis effect when all kinds of data samples were unevenly distributed in fault diagnosis,support vector data description(SVDD)was proposed based on support vector machine(SVM),the extension of SVDD to multiple decision and the limitations of various extension methods were also studied.A multi-decision SVDD weighted by K-neighbor probability was proposed.The ensemble empirical mode decomposition(EEMD)was used to decompose the original signal,and the information content of each intrinsic mode function(IMF)was calculated and taken as a characteristic.The third-party experimental data were used to test the identification accuracy of k-neighbor probability support vector data description(KNP-SVDD)method in various fault categories.The results indicate that the method can effectively identify the location and severity of the fault,and the superiority of the method is proved by comparing with other classification methods.
作者
陈宇晨
何毅斌
戴乔森
刘湘
贺苏逊
CHEN Yu-chen;HE Yi-bin;DAI Qiao-sen;LIU Xiang;HE Su-xun(Mechanical and Electrical Engineering,Wuhan Institute of Technology,Wuhan 430205,China)
出处
《机电工程》
CAS
北大核心
2021年第1期55-61,共7页
Journal of Mechanical & Electrical Engineering
基金
湖北省科技厅重大专项资助项目(2016AAA056)
化工装备强化与本质安全湖北省重点实验室开放基金资助项目(2018KA01)。
关键词
故障诊断
支持向量机
集合经验模态分解
本征模函数
K相邻概率支持向量数据描述
fault diagnosis
support vector machine(SVM)
ensemble empirical mode decomposition(EEMD)
intrinsic mode function(IMF)
K-neighbor probability support vector data description(KNP-SVDD)
