基于VMD与KFCM的柴油机故障诊断算法

Diesel Engine Fault Diagnosis Method Based on Optimized Variational Mode Decomposition and Kernel Fuzzy C-means Clustering

针对柴油机的故障诊断问题,提出了一种基于变分模态分解(variational mode decomposition,简称VMD)与核模糊C均值聚类算法(kernel fuzzy C-means clustering,简称KFCM)联合的故障诊断方法。首先,针对VMD算法中分解层数K的选择问题进行了自适应优化;然后,从优化VMD算法的分解结果中选取3个关键分量计算最大奇异值,并将其作为3维的特征向量输入KFCM算法中进行分类识别;最后,对仿真信号以及某型柴油机的模拟故障实验信号使用优化VMD、传统VMD和经验模态分解(empirical mode decomposition,简称EMD)方法分别进行分解与识别。结果表明,笔者提出的方法明显改善了模态混叠现象,提高了模式识别的诊断正确率,提出的联合算法具有更好的应用前景。

To solve the diesel engine fault diagnosis problem,a fault diagnosis method based on the combination of variational mode decomposition(VMD)and kernel fuzzy C-means clustering(KFCM)is proposed.This paper optimizes the selection of decomposition level Kin VMD algorithm,and proposes an adaptive choosing method for K.Then,three key components are selected from the decomposition results of the optimized VMD algorithm to calculate the maximum singular values,which are input into the KFCM algorithm as three-dimensional eigenvectors for classification and recognition.The optimized VMD method,VMD method and empirical mode decomposition(EMD)method are used to decompose and recognize the simulated signal and the experimental data of a diesel engine.The results show that the proposed method obviously improves the accuracy of pattern recognition.The joint algorithm proposed in this paper has better application prospects.