基于小波包对数能量图的滚动轴承故障诊断方法  

Rolling bearing fault diagnosis method via wavelet packet logarithmic-energy map

作　　者：王娜[1,2] 崔月磊 李杨 王子从 WANG Na;CUI Yue-lei;LI Yang;WANG Zi-cong(School of Control Science and Engineering,Tiangong University,Tianjin 300387,China;Key Laboratory of Intelligent Control of Electrical Equipment,Tianjin 300387,China)

机构地区：[1]天津工业大学控制科学与工程学院,天津300387 [2]天津市电气装备智能控制重点实验室,天津300387

出　　处：《吉林大学学报(工学版)》2025年第2期494-502,共9页Journal of Jilin University:Engineering and Technology Edition

基　　金：天津市重点研发计划项目(19YFHBQY00040);天津大学微光机电系统技术教育部重点实验室开放基金项目(MOMST2016-4)。

摘　　要：针对滚动轴承的故障诊断问题,提出一种基于小波包对数能量图的诊断方法。首先,改进并提出新的小波包节点对数能量公式,以克服传统小波包能量公式中参数确定烦琐且主观性强的缺点,提高对高频故障的辨识度和对低频故障类别的区分度,以实现对初始时频域特征的充分提取;其次,利用格拉姆角和场思想实现由一维特征到二维图像特征的转换,以此构造出基于小波包的对数能量图特征,其进一步考虑相邻特征之间的空间信息,从而实现对初始时频域特征的优化,提高了所得特征的显著性。在此基础上,通过残差网络改善故障诊断分类结果的精度;最后,通过凯斯西储大学的标准滚动轴承数据集仿真验证可知,本文方法构建的故障诊断模型具有较高的诊断精度,并且泛化能力较强。For the fault diagnosis on rolling bearing,a method via wavelet packet logarithmic-energy mapis proposed.Firstly,a new wavelet packet node logarithmic energy formula is improved and presented toovercome the complexity and subjectivity of parameters in the traditional ones.Thus the high-frequencyfaults and the low-frequency faults are easily identified.As a result,the initial time-frequency features areextracted adequately.Secondly,the idea of Gramian angular summation field is used to transform thefeatures from one-dimension data to two-dimension picture.Therefore the features via wavelet packetlogarithmic-energy map are constructed.In them,the space information among the adjacent features areconsidered further.So the optimization for the initial time-frequency features is completed and theirsignificance are increased.On this basis,the residual network is applied to enhance the accuracy of thepresented approach.Finally,the higher accuracy of diagnosis and the greater generalization ability of theproposed method is verified by the standard rolling bearing data set of Case Western Reserve University.

关 键 词：故障诊断 特征提取 滚动轴承 格拉姆角和场 小波包对数能量图 残差神经网络 

分 类 号：TH113.1[机械工程—机械设计及理论] TH17