基于VMD-AR谱的多轴转子系统故障特征信息提取方法

Method for extracting fault feature information of multi-axis rotor system based on VMD-AR spectrum

为了解决现有分解方法在近频部分产生模态混叠的问题,提高时变参数模型对故障信号直接诊断的准确性,研究了多轴转子系统的典型故障信号分析方法.提出了基于变分模态分解(VMD)和AR谱相结合的故障特征信号分析方法;采取瞬时频率均值法选取VMD中的分解模式数k.经VMD分解产生固有模态函数(IMFs),采用AR谱对IMFs分量进行特征提取,分析典型故障模式相对应的特征频带.结果表明:基于VMD-AR谱的故障特征提取方法解决了分解模式数k的选取难题,避免k值的经验选取;VMD能够抑制信号分解近频部分的模态混叠,而AR模型克服了Hilbert分离算法存在的加窗效应,在频带划分上具有较强的分辨率.该方法可有效进行故障特征提取,并为改进的粒子群优化算法的混合核支持向量机算法提供特征信息.

To solve the problem of modal aliasing in the near-frequency part of the existing decomposition method and improve the accuracy of the time-varying parameter model for direct diagnosis of the fault signal,the typical fault signal analysis method of the multi-axis rotor system was investigated.A method for analyzing fault characteristic signals was proposed based on the combination of variational mode decomposition(VMD)and AR spectrum.The number of decomposition modes k in VMD was selected by the instantaneous frequency mean method.After the VMD decomposition,the inherent modal functions(IMFs)were generated.The AR spectrum was used to extract the features of IMFs,and the characteristic frequency bands corresponding to the typical failure modes were analyzed.The results show that the problem of selecting the decomposition mode number k can be solved by the fault feature extraction method based on VMD-AR spectrum,and the k value can be avoided in the empirical selection.The modal aliasing of the near-frequency part of the signal decomposition can be suppressed by VMD.The windowing effect of Hilbert separation is overcome by the AR model,which has strong resolution in frequency band division.The proposed method can effectively extract fault features and provide feature information for the hybrid kernel support vector machine algorithm of the improved particle swarm optimization algorithm.