Rolling Element Bearing Fault Diagnosis Using Improved Manifold Learning

Abstract

Fault feature can be extracted by traditional manifold learning algorithms, which construct neighborhood graphs by Euclidean distance (ED). It is difficult to get an excellent dimensionality reduction result when processed data has strong correlations. In order to improve the effect of dimensionality reduction and increase accuracy of bearing fault diagnosis in mechanical systems, an improved manifold learning method based on Mahalanobis distance (MD) is proposed. In this paper, we use time-domain analysis and frequency-domain analysis to construct high-dimensional feature vectors in the first step. Then, MD is used to replace ED in neighborhood construction of manifold learning. After using the improved manifold learning method, low-dimensional feature vectors can be extracted. Finally, fault diagnosis of rolling element bearing can be made by applying the K-nearest neighbor classifier. In part of experiment, to verify the efficiency of the improved manifold learning methods, artificial data sets and rolling element bearing fault data are adopted. The experimental comparison results of the improved manifold learning algorithm and the traditional algorithm prove that the proposed method is more effective in rolling element bearing fault diagnosis.