基于图拉普拉斯矩阵和改进K均值聚类的滚动轴承故障诊断

Fault diagnosis of rolling bearings based on graph Laplacian matrix and improved K-means clustering

由于轴承的振动信号中往往蕴含大量的干扰信号,高效提取故障特征并进行分类识别是轴承诊断工作的关键所在。传统的故障特征提取方法往往需要多种表征不同故障的指标集合,本文提出了一种基于马氏距离加权的Laplace矩阵和改进K均值聚类的轴承故障诊断方法。首先将轴承的时域离散信号映射到图形域以获得图信号,通过马氏距离加权和图信号的代数形式Laplace矩阵得到表征轴承不同故障状态的特征指标集合,再应用改进K均值聚类思想将特征指标集合进行评估和分类,以实现通过单一指标对不同故障状态轴承准确分类的目的。实验结果表明,基于马氏距离加权的Laplace矩阵和改进K均值聚类的轴承诊断方法能够有效提取不同故障的特征指标并进行准确分类,同时,该方法在单一指标分类上正确率远高于传统故障特征提取方法。

Because a lot of jamming signals are often included in the bearing vibration signals, the key to bearing diagnosis is to extract fault features efficiently and classify them. Traditional methods of fault feature extraction often need a variety of index sets to represent different faults. In this paper, a method of bearing fault diagnosis based on Laplacian matrix weighted by Mahalanobis distance and improved K-means clustering is proposed. Firstly, the time domain discrete signal of the bearing is mapped to the graph domain to obtain the graph signal, and the set of characteristic indexes representing different bearing fault states is obtained by using Laplacian matrix in an algebraic form of the graph signal weighted by Mahalanobis distance. Then, the improved K-means clustering idea is applied to evaluate and classify the set of characteristic indexes, to realize the classification and recognition of different bearing fault states in the case of single index. The experimental results show that the method of bearing diagnosis based on Mahalanobis distance weighted by Laplacian matrix and improved K-means clustering can effectively extract and precisely classify the characteristic indexes of different bearing faults. At the same time, the accuracy of this method in single index classification is much higher than that of traditional fault feature extraction methods.