Fault Diagnosis of Supervision and Homogenization Distance Based on Local Linear Embedding Algorithm

Guangbin Wang,Qinyi Chen,Yilin He,Jun Luo

doi:10.1155/2015/981598

Abstract

In view of the problems of uneven distribution of reality fault samples and dimension reduction effect of locally linear embedding (LLE) algorithm which is easily affected by neighboring points, an improved local linear embedding algorithm of homogenization distance (HLLE) is developed. The method makes the overall distribution of sample points tend to be homogenization and reduces the influence of neighboring points using homogenization distance instead of the traditional Euclidean distance. It is helpful to choose effective neighboring points to construct weight matrix for dimension reduction. Because the fault recognition performance improvement of HLLE is limited and unstable, the paper further proposes a new local linear embedding algorithm of supervision and homogenization distance (SHLLE) by adding the supervised learning mechanism. On the basis of homogenization distance, supervised learning increases the category information of sample points so that the same category of sample points will be gathered and the heterogeneous category of sample points will be scattered. It effectively improves the performance of fault diagnosis and maintains stability at the same time. A comparison of the methods mentioned above was made by simulation experiment with rotor system fault diagnosis, and the results show that SHLLE algorithm has superior fault recognition performance.

Highlights

With the development of the modernization process, the structure of mechanical equipment has been more and more increasingly sophisticated while the degree of automation and function of realization grow increasingly stronger
A comparison of the methods mentioned above was made by simulation experiment with rotor system fault diagnosis, and the results show that SHLLE algorithm has superior fault recognition performance
Classic manifold learning methods such as isometric feature mapping (ISOMAP) [1], local linear embedding (LLE) [2], local tangent space alignment (LTSA) [3, 4], and Laplacian eigenmap (LE) [5, 6] algorithm are mainly applied to the fields of data mining, image processing, pattern recognition, and information retrieval [7,8,9]

Summary

Introduction

With the development of the modernization process, the structure of mechanical equipment has been more and more increasingly sophisticated while the degree of automation and function of realization grow increasingly stronger. For the extraction of weak feature of early fault, Li et al [13] proposed the early fault diagnosis and feature extraction methods of the rolling bearing based on manifold learning, which improved the fault pattern classification performance. In this study with rotating machinery as the research object, an improved local linear embedding algorithm of homogenization distance (HLLE) is proposed on the basis of the research of locally linear embedding (LLE) method to solve the problems that the real fault samples set are not distributed evenly and the phenomena that LLE is affected by the neighboring points. The rotor system fault simulation experiment is used to analyze and validate the effectiveness of fault diagnosis

Locally Linear Embedding Algorithm of Homogenization Distance

Reconstruct with linear weights

Locally Linear Embedding Algorithm of Supervision and Homogenization Distance

Simulation Experiment of Rotor System Fault

Fault Diagnosis Based on SHLLE

Conclusion