复合多尺度反向散布熵在轴承故障诊断中的应用  

Application of CMRDE in bearing fault diagnosis

作　　者：陈焱 郑近德[1] 潘海洋[1] 童靳于[1] CHEN Yan;ZHENG Jinde;PAN Haiyang;TONG Jinyu(School of Mechanical Engineering,Anhui University of Technology,Ma’anshan 243002,China)

机构地区：[1]安徽工业大学机械工程学院,安徽马鞍山243032

出　　处：《振动与冲击》2022年第19期55-63,共9页Journal of Vibration and Shock

基　　金：国家自然科学基金(51975004);安徽省自然科学基金项目资助(2008085QE215)。

摘　　要：滚动轴承发生故障时,其振动信号往往表现出非线性和非平稳特征。反向散布熵(reverse dispersion entropy,RDE)能够有效衡量振动信号的复杂性变化和非线性动力学突变行为,但是单一尺度的RDE值并不能完全反映振动信号的复杂性和非线性特征。对此,受多尺度熵启发,同时针对传统多尺度粗粒化方式的不足,提出了复合多尺度反向散布熵(composite multi-scale reverse dispersion entropy,CMRDE)。通过仿真信号分析,将CMRDE与多尺度反向散布熵(multi-scale reverse dispersion entropy,MRDE)和RDE进行对比,结果表明:CMRDE不仅能反映不同尺度下信号复杂度的差异,且变化更平缓、波动更小。在此基础上,将CMRDE应用于滚动轴承故障特征提取,提出了一种基于CMRDE、集合经验模态分解和布谷鸟搜索算法优化支持向量机的滚动轴承故障诊断方法。将所提方法应用于滚动轴承试验数据分析,并通过与现有方法进行对比,结果表明:相较所对比的方法,所提方法能有效识别轴承故障类型,提取的故障特征误差更小、故障识别率更高。When a rolling bearing fails,its vibration signal often has nonlinear and nonstationary characteristics.Reverse dispersion entropy(RDE)can effectively measure complexity change and nonlinear dynamic catastrophe behavior of vibration signals,but single scale RDE value can’t fully reflect complexity and nonlinear characteristics of vibration signals.Here,inspired by multi-scale entropy and aiming at shortcomings of traditional multi-scale coarsening mode,a composite multi-scale reverse dispersion entropy(CMRDE)was proposed.Through simulation signal analysis,CMRDE was compared with multi-scale reverse dispersion entropy(MRDE)and RDE.The results showed that CMRDE can not only reflect difference of signal complexity at different scales,but also change more smoothly and fluctuate less.Then,CMRDE was applied in fault feature extraction of rolling bearing,a rolling bearing fault diagnosis method based on CMRDE,ensemble empirical mode decomposition(EEMD)and support vector machine optimized with Cuckoo search(CS-SVM)algorithm was proposed.Finally,the proposed method was applied to analyze rolling bearing experimental data,and the results were compared with those obtained using the existing methods.It was shown that compared with other methods,the proposed method can effectively identify bearing fault types,the extracted fault feature errors are smaller,and its fault recognition rate is higher.

关 键 词：反向散布熵 复合多尺度反向散布熵 滚动轴承 故障诊断 

分 类 号：TH165.3[机械工程—机械制造及自动化]