A Novel Method for Predicting Fault Labels of Roller Bearing by Generalized Laplacian Matrix

Jiawei Gu,Chaofan Hu,Zexi Luo,Yanxue Wang

doi:10.1109/access.2020.3048000

Abstract

Because mechanical failures are accompanied by contingency and randomness, fault data is often difficult to obtain, and fault labels are also difficult to assign. The lack of data and fault labels have become important issues that restrict the development of fault diagnosis. The paper proposed a generalized Laplacian label prediction (GLLP) algorithm, which mainly uses the generalized Laplacian matrix and calculated a new locally smooth term. Therefore, data points with ambiguous and unclear labels will be assigned a small label value, while samples with more certain labels can get a more confident label value. The effectiveness of the method is verified on the public dataset and the real test rig dataset, and it is expected that this method can be extended to more complex mechanical system fault diagnosis.

Highlights

Mechanical systems are similar to medical systems
In this study, a novel label prediction method based on the generalized Laplacian matrix has been proposed
The generalized Laplacian label prediction (GLLP) is a new attempt of the fault label prediction algorithm

Summary

INTRODUCTION

[13], [13]–[18] Proposed intelligent fault diagnosis methods based on semi-supervised learning, which assign the same labels with similar features by buiding similarity matrix. In response to this ambiguity, a generalized Laplacian matrix [19] is proposed to define a new smooth term. The main contributions of this research can be summarized as follows: 1) Solving the dilemma of insufficient data labels for fault diagnosis; 2) A new smooth term is constructed by adopting generalized Laplace matrix; 3) The proposed GLLP can be regarded as a unified framework for graph-based label propagation methods.

INFERENCE MODEL

INDUCTIVE MODEL

EXPERIMENTAL ANALYSIS The experimental analysis is divided into two parts

Findings

CONCLUSION