趋势引导VMD算法的滚动轴承故障诊断方法研究  

Research on Fault Diagnosis of Rolling Bearing Based on Trend-Guided VMD

作　　者：吴震 李欢 王涛 WU Zhen;LI Huan;WANG Tao(Institute of Intelligent Manufacturing and Automotive,Chongqing Technology and Business Institute,Chongqing 401520,China;School of Intelligent Engineering,Chongqing College of Mobile Communication,Chongqing 401520,China;State Key Laboratory of Traction Power,Southwest Jiaotong University,Sichuan Chengdu 610031,China)

机构地区：[1]重庆工商职业学院智能制造与汽车学院,重庆401520 [2]重庆移通学院智能工程学院,重庆401520 [3]西南交通大学牵引动力国家重点实验室,四川成都610031

出　　处：《机械设计与制造》2024年第7期72-76,83,共6页Machinery Design & Manufacture

基　　金：重庆市教育委员会科学技术研究重点项目(KJZD-K202004001)。

摘　　要：针对强噪声背景下滚动轴承故障特征提取困难且VMD算法相关参数的确定过于依赖先验知识的问题,提出了改进VMD算法。该算法的核心首先是提出频谱趋势算法用于寻找共振频带边界从而确定频带的数量k和各个频带的带宽,然后根据取值方式预估各个频带对应的惩罚因子,最后将确定参数代入VMD算法中分析分解结果。通过仿真信号和试验信号验证,该方法可以较为准确的确定频带数量和初始惩罚因子,提升了VMD算法的准确性和自适应性。Aiming at the problem that it is difficult to extract the fault features of rolling bearings under the background of strong noise and the determination of the relevant parameters of the VMD algorithm relies too much on the prior knowledge,an improved VMD algorithm is proposed.The core of the algorithm is to first propose a spectral trend algorithm to find the boundary of the resonance frequency band to determine the number k of the frequency bands and the bandwidth of each frequency band,then estimate the penalty factor corresponding to each frequency band according to the value method,and finally substitute the determined parameters into the VMD algorithm.Analyze the decomposition results.Through simulation signal and experimental signal verification,this method can more accurately determine the number of frequency bands and initial penalty factor,which improves the accuracy and adaptability of VMD algorithm.

关 键 词：故障诊断 滚动轴承 变分模态分解 频谱趋势 惩罚因子 

分 类 号：TH17[机械工程—机械制造及自动化] TH16