ALIF-NLM轴承微弱故障特征提取方法  

ROLLING BEARING WEAK FAULT FEATURE EXTRACTION METHOD WITH ALIF⁃NLM

作　　者：汪应 宋宇博[1] 朱大鹏[2] WANG Ying;SONG YuBo;ZHU DaPeng(Mechatronics T&R Institute,Lanzhou Jiaotong University,Lanzhou 730070,China;School of Traffic and Transportation,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学机电技术研究所,兰州730070 [2]兰州交通大学交通运输学院,兰州730070

出　　处：《机械强度》2024年第5期1026-1035,共10页Journal of Mechanical Strength

基　　金：国家自然科学基金项目(51765028)资助。

摘　　要：针对强噪声背景下滚动轴承早期微弱故障特征难以提取的问题,结合自适应局部迭代滤波(Adaptive Local Iterative Filter,ALIF)和非局部均值(Non-Local Means,NLM)去噪方法的优势,提出了一种ALIF-NLM轴承微弱故障特征提取方法。首先,构建了加权峭度-能量比准则来筛选ALIF分解的本征模态函数(Intrinsic Mode Function,IMF)分量并重构信号。其次,结合峭度对冲击信号的敏感性同能量熵对信号能量分布均匀性和复杂程度的评价性能构建最小能量熵-峭度比指标,并以该指标为适应度函数,利用粒子群优化(Particle Swarm Optimization,PSO)算法实现了NLM方法中参数组合的自适应选取。最后,利用自适应NLM对重构信号进行故障特征提取。仿真和试验分析结果表明,该方法能有效提取出强噪声背景下的滚动轴承微弱故障特征信息。Aiming at the problem that the early weak fault feature was difficult to extract of rolling bearing under the strong noise background,combined with the advantages of adaptive local iterative filter(ALIF)and non⁃local means(NLM)method,an ALIF⁃NLM bearing weak fault feature extraction method was proposed.Firstly,a weighted kurtosis⁃energy ratio criterion was constructed to filter the intrinsic mode function(IMF)components of the ALIF decomposition and reconstruct the signal.Secondly,the minimum energy entropy⁃kurtosis ratio index was constructed by combining the sensitivity of kurtosis to the impact signal with the evaluation performance of energy entropy to the uniformity and complexity of signal energy distribution,and using this index as the fitness function,the adaptive selection of parameter combinations in NLM method was realized by particle swarm optimization(PSO)algorithm.Finally,the fault feature of the reconstructed signal was extracted with the adaptive NLM.The simulation and experimental results show that this method can effectively extract the weak fault feature information of rolling bearing under the strong noise background.

关 键 词：强噪声 滚动轴承 自适应局部迭代滤波 粒子群优化 非局部均值去噪 微弱特征提取 

分 类 号：TH133.33[机械工程—机械制造及自动化]