基于VMD的Volterra模型奇异值熵的转子故障诊断方法

针对转子故障信号的非平稳性以及敏感故障特征无法有效提取的问题,将变分模态分解(variational mode decomposition,VMD)的Volterra模型和奇异值熵相结合,提出一种故障诊断方法。对影响VMD分解准确性的参数选取方法进行了深入研究,给出了相关问题的解决策略。首先,对不同工况下转子实测信号进行VMD分解,利用能量熵增量选取对故障特征敏感的固有模态函数(intrinsic mode function,IMF)进行相空间重构,以建立Volterra自适应预测模型,将模型参数作为初始特征向量矩阵。然后,对初始特征向量进行奇异值分解以获取奇异值熵和奇异值特征向量矩阵,用于描述转子的故障特征。最后,采用模糊C均值(fuzzy c-means,FCM)算法对转子工作状态和故障类型进行识别。试验结果表明,所提方法可有效实现转子故障的特征提取及类型识别。通过同经集合经验模态分解(ensemble empirical mode decomposition,EEMD)相比,证明了该方法具有更有效的故障特征提取性能,是一种可行的方法。

Rotor fault diagnosis method of singular value entropy of Volterra model based on VMD

Aiming at the non-stationarity of rotor fault signal and the inability to effectively extract sensitive fault features,a fault diagnosis method was proposed by combining the Volterra model of variational mode decomposition(VMD)and singular value entropy.The parameter selection methods affecting the accuracy of VMD decomposition were deeply studied,and the solutions to the related problems were given.Firstly,the measured rotor signals under different working conditions were decomposed by VMD,and the intrinsic mode function(IMF)sensitive to fault characteristics was selected by using the increment of energy entropy for phase space reconstruction,so as to establish the Volterra adaptive prediction model,and the model parameters were used as the initial eigenvector matrix.Then,the initial eigenvector was decomposed by singular value decomposition to obtain singular value entropy and singular value eigenvector matrix,which were used to describe the fault characteristics of rotor.Finally,the fuzzy C-means(FCM)algorithm was used to identify the rotor working state and fault type.The experimental results show that the proposed method can effectively realize the feature extraction and type recognition of rotor fault.Compared with ensemble empirical mode decomposition(EEMD),it is proved that this method has more effective fault feature extraction performance and is a feasible method.