基于VMD-MCKD的微弱故障信号降噪及冲击特征增强方法  

Method for denoising weak fault signals and enhancing impact characteristics based on VMD-MCKD

作　　者：费红博 张超[1,2] 吴乐 徐帅 张敬 FEI Hongbo;ZHANG Chao;WU Le;XU Shuai;ZHANG Jing(School of Mechanical Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,China;Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems in Inner Mongolia Autonomous Region,Baotou 014010,China)

机构地区：[1]内蒙古科技大学机械工程学院,内蒙古包头014010 [2]内蒙古自治区机电系统智能诊断与控制重点实验室,内蒙古包头014010

出　　处：《机电工程》2025年第2期237-246,共10页Journal of Mechanical & Electrical Engineering

基　　金：国家自然科学基金资助项目(52365014);中央引导地方科技发展资金项目(2022ZY0221)。

摘　　要：针对强噪声背景下滚动轴承早期故障冲击信号微弱,故障特征难以提取的问题,提出了一种基于参数自适应变分模态分解(VMD)与最大相关峭度解卷积(MCKD)的滚动轴承故障诊断方法(微弱故障信号降噪及冲击特征增强方法)。首先,采用时频域差值信息引导VMD,并引入相似系数差值和能量差值比作为迭代收敛条件,重新设定了适用于信号分解的终止准则;然后,采用改进的减法平均优化算法,对MCKD中的解卷周期T、移位数M和滤波器长度L进行了优化,确保了参数组合的最佳性;借助MCKD方法的冲击特征提取能力,精确获取了目标周期性冲击信号;最后,依托内蒙古科技大学机械工程学院配备的HZXT-DS-003型双跨转子滚动轴承试验台,构建了故障轴承数据集,对基于VMD-MCKD的滚动轴承故障诊断方法的有效性进行了验证。研究结果表明:采用该方法能有效抑制噪声,显著增强信号的周期冲击特性、故障特征频率及其倍频,从而完成了对滚动轴承早期微弱故障的准确诊断;与其他方法相比,该方法在频谱中更为突出地展现故障特征频率及其倍频峰值,且信噪比提升了78%;此外,即使在不同信噪比的噪声环境下,该方法仍能保持卓越的信号处理能力。Aiming at the issue of weak early fault impulse signals in rolling bearings under high-noise environments and the difficulty in extracting fault features,a rolling bearing fault diagnosis method based on the combination of parameter-adaptive variational mode decomposition(VMD) and maximum correlated kurtosis deconvolution(MCKD) was proposed(method for denoising weak fault signals and enhancing impact characteristics).First,VMD was guided by time-frequency domain difference information,and similarity coefficient difference and energy difference ratio were introduced as iterative convergence conditions to redefine the termination criteria suitable for signal decomposition.Next,an improved subtraction mean optimization algorithm was employed to optimize the deconvolution period T,shift number M,and filter length L in MCKD,ensuring the optimal parameter combination.The impact feature extraction capability of the MCKD method was then utilized to accurately capture the target periodic impulse signal.Finally,a fault bearing dataset was constructed using the HZXT-DS-003 dual-span rotor rolling bearing test rig at the School of Mechanical Engineering,Inner Mongolia University of Science and Technology.The effectiveness of the fault diagnosis method for rolling bearings based on VMD-MCKD was verified.The research results indicate that the proposed method effectively suppresses noise,significantly enhances the periodic impact characteristics of the signal,and the fault characteristic frequency and its harmonics,thereby enabling the accurate diagnosis of early weak faults in rolling bearings.Comparing to other methods,this method more prominently displayed the fault characteristic frequency and its harmonic peaks in the spectrum,with a 78% improvement in the signal-to-noise ratio.Additionally,the method maintained excellent signal processing capability even in noise environments with varying signal-to-noise ratios.

关 键 词：滚动轴承 早期故障特征 变分模态分解 最大相关峭度解卷积 参数自适应 周期性冲击信号 

分 类 号：TH133.3[机械工程—机械制造及自动化]