自适应噪声极值加权模态分解及其在低速滚动轴承故障诊断中的应用  

Complete Ensemble Extreme-point Weighted Mode Decomposition with Adaptive Noise and its Application to Fault Diagnosis of Low Speed Rolling Bearings

作　　者：苏缪涎 郑近德[1,2] 潘海洋 童靳于[1] 刘庆运[1] 潘紫微[1] Su Miaoxian;Zheng Jinde;Pan Haiyang;Tong Jinyu;Liu Qingyun;Pan Ziwei(School of Mechanical Engineering,Anhui University of Technology,Anhui Ma′anshan 243032,China;Anhui Key Laboratory of Mine Intelligent Equipment and Technology,Anhui University of Science&Technology,Anhui Huainan 232001,China)

机构地区：[1]安徽工业大学机械工程学院,安徽马鞍山243032 [2]安徽理工大学矿山智能装备与技术安徽省重点实验室,安徽淮南232001

出　　处：《机械科学与技术》2020年第11期1662-1670,共9页Mechanical Science and Technology for Aerospace Engineering

基　　金：国家重点研发计划项目(2017YFC0805100);国家自然科学基金项目(51975004,51505002);安徽省高校自然科学研究重点项目(KJ2019A053,KJ2019A092)资助。

摘　　要：极值加权模态分解(Extreme-point weighted mode decomposition,EWMD)是在经验模态分解(Empirical mode decomposition,EMD)的基础上提出的一种自适应信号分解方法,能够改善EMD的分解能力。针对于EWMD的模态混叠问题,借鉴噪声辅助分解思想,提出了自适应噪声极值加权模态分解(Complete ensemble extreme-point weighted mode decomposition with adaptive noise,CEEWMDAN)。CEEWMDAN通过向原始信号中添加辅助噪声,用改进的均值曲线构造方式提取内禀模态函数,有效地抑制模态混叠,且分解结果对集成次数的依赖性小,在保证分解精度的前提下减少了计算量。通过仿真实验数据分析验证了CEEWMDAN在提高分解性能和抑制模态混叠方面的有效性。提出了一种基于CEEWMDAN和快速谱峭度的低速滚动轴承故障诊断方法,并应用于实测数据分析。结果表明所提出的方法能够有效地识别低速滚动轴承故障。Extreme-point weighted mode decomposition(EWMD)is an adaptive signal processing method based on empirical mode decomposition(EMD).Aiming at mode mixing problem of EWMD,the complete ensemble extreme-point weighted mode decomposition with adaptive noise(CEEWMDAN)is proposed in this paper.CEEWMDAN extracts intrinsic mode function by adding the assisted noise to original signal and using the improved mean curve,which effectively restrains the mode mixing and reduces the computational complexity.Its effectiveness is verified by simulation experimental data analysis.Finally,a fault diagnosis method for low-speed rolling bearings based on CEEWMDAN and fast spectral kurtosis is proposed and applied to the measured data analysis.The results show that the proposed fault diagnosis method can effectively identify low-speed rolling bearing faults.

关 键 词：EMD EWMD 模态混叠 滚动轴承 故障诊断 低速 

分 类 号：TH17[机械工程—机械制造及自动化]