基于参数优化变分模态分解的滚动轴承微弱故障诊断研究

为了提高变分模态分解(VMD)对滚动轴承微弱故障特征提取的准确性,提出了一种基于参数优化VMD与奇异值分量及其熵相结合的滚动轴承故障诊断方法。该方法通过寻优算法确定VMD的模态数K和二次惩罚因子α;根据余弦-标准差指标提取VMD典型本征模态分量(IMF);计算IMF奇异值及其熵,并利用计算结果分别判断滚动轴承的不同故障状态。结合美国西储大学轴承振动信号数据,实验结果表明:相比经验模态分解奇异值故障诊断方法,基于参数优化VMD奇异值故障诊断方法能更明显地识别滚动轴承的不同故障类型,为区分滚动轴承微弱故障提供了一种可行的诊断思路。

Research on Weak Fault Diagnosis of Rolling Bearing Based on Parameter-optimized Variational Mode Decomposition

In order to improve the accuracy of weak fault features extraction of rolling bearings based on variational modal decomposition(VMD),a fault diagnosis method of rolling bearings were proposed based on parameter optimization VMD combined with singular value component and its entropy.In this method,the modal number K and the second penalty factorαof VMD were determined by an optimization algorithm,the typical intrinsic mode functions(IMF)of VMD were extracted according to cosine-standard deviation index.The IMF singular value and its entropy were calculated,and the results were used to judge the different fault states of rolling bearing.Combined with the experimental results of bearing vibration signal data from Case Western Reserve University in the U.S.,it is shown that the singular value fault diagnosis method based on parameter optimization of VMD can identify different fault types of rolling bearings more clearly compared with the empirical modal decomposition singular value fault diagnosis method.The study provides a feasible diagnostic approach to distinguish the weak faults of rolling bearings.