基于优化VMD与欧氏距离的柴油机故障识别

为实现利用单一通道信号通过同一方法区分多种发动机故障的目的，笔者对现有算法进行了优化以提取振动信号中的故障特征。首先，针对变分模式分解（variational mode decomposition,简称VMD）的分解层数选择困难问题，文中以几种不同类型故障的频率特征为基础，优化了其中心频率迭代初始值，在保证准确性的前提下提高了算法的计算效率与简便性；然后，利用鲁棒性独立分量分析（Robust independent component analysis,简称Robust ICA）对VMD处理结果再次分解，分析发动机中可能存在的不同振源的同频率信号，并将两个阶段分解结果重构信号的四阶累积量作为故障判定指标。结果表明：以模糊C均值聚类（fuzzy C-means clustering,简称FCM）确定的聚类中心为参考点，利用各个工况点与喷油故障聚类中心的欧氏距离区分故障类型，取得了较高的正确率。

Engine Faults Detection Based on Optimized VMD and Euclidean Distance

In light of the problem to distinguish multiple engine faults through the same method using a single channel signal, the existing algorithms are optimized to extract fault characteristics from vibration signals. First, in view of the difficulty in selecting the decomposition level of the variational mode decomposition (VMD) decomposition levels selection, the initial value of the center frequency iteration is optimized based on the frequency characteristics of several different types of faults, which improves calculation efficiency and convenience while ensuring accuracy. Then, the robust independent component analysis (Robust ICA) is introduced to analyze different signal sources in the same frequency. The fourth-order cumulant of the restructured signals from VMD and Robust ICA is taken as failure indexes. Finally, the cluster center determined by fuzzy C-means clustering is used as the reference point. The Euclidean distance between each test points and the center is used to distinguish fault types. The results show that this method achieves high recognition rate.