SE-KSD优化的FRFT-VMD轴承故障诊断方法  

SE-KSD Optimized FRFT-VMD Bearing Fault Diagnosis Method

作　　者：韦明辉 江丽霞 涂凤秒 姜蓬勃 WEI Minghui;JIANG Lixia;TU Fengmiao;JIANG Pengbo(School of Mechanical Engineering,Southwest Petroleum University,Chengdu 610500,China;Oil and Gas Equipment Technology Sichuan Province Science and Technology Resource Sharing Service Platform,Chengdu 610500,China)

机构地区：[1]西南石油大学机电工程学院,成都610500 [2]石油天然气装备技术四川省科技资源共享服务平台,成都610500

出　　处：《机械科学与技术》2024年第2期265-273,共9页Mechanical Science and Technology for Aerospace Engineering

基　　金：国家自然科学基金项目(51804267);中国石油大学石油资源与勘探国家重点实验室项目(北京)(PRP/open-1610)。

摘　　要：提出了一种基于样本熵(SE)和峭度均方差(KSD)优化的分数阶变分模态分解(FRFT-VMD)的故障特征提取方法,同时结合随机森林(RF)分类器对故障进行自动识别分类。针对分数阶傅里叶变换中阶次选择对于数据可分性影响较大的问题,提出利用搜寻样本熵最小值来得到分数阶最优阶次,使得数据中重叠交叉部分在分数域内能更好分离。同时利用峭度均方差准则寻找变分模态分解最优参数使变分模态分解效果更好。通过数据库和实测数据的研究结果表明,该方法提取的信号包含更多和更明显的故障特征频率,大幅度提高了滚动轴承不同状态的故障诊断准确率。A fault feature extraction method based on sample entropy(SE)and kurtosis standard deviation(KSD)optimization of fractional variational modal decomposition(FRFT-VMD)is proposed,and a random forest(RF)classifier is combined to perform fault detection.Automatic recognition of classification.Aiming at the problem that the choice of order in the fractional Fourier transform has a greater impact on the separability of data,it is proposed to search for the minimum entropy of the sample to obtain the optimal order of the fractional order.Makes the overlapped part of the data better separated in the score domain.At the same time,the kurtosis standard deviation criterion is used to find the optimal parameters of the variational modal decomposition to make the effect of the variational modal decomposition better.The research results based on database data and measured data show that the signals extracted by this method contain more and more obvious fault characteristic frequencies,which greatly improves the fault diagnosis accuracy of rolling bearings in different states.

关 键 词：分数阶FOURIER变换 变分模态分解 特征提取 故障诊断 

分 类 号：TG156[金属学及工艺—热处理]