An Imbalanced Fault Diagnosis Method Based on Multi-Sensor Selection and Graph Attention Mechanism

TL;DR

This paper introduces a new method for fault diagnosis in bearings using sensor data and graph attention networks to handle imbalanced data.

Contribution

The novel SCGAT method combines sensor selection and graph attention mechanisms to improve fault diagnosis accuracy in imbalanced datasets.

Findings

The SCGAT method improves diagnostic accuracy and stability in imbalanced bearing datasets.

Sensor sensitivity and correlation analyses enhance feature extraction from multi-sensor data.

Experimental validation confirms the effectiveness of the proposed method on a power transmission platform.

Abstract

Severe diagnostic errors are often caused by the significant imbalance between normal and fault data in bearing datasets. To solve this challenge, a graph attention convolutional neural network based on sensitivity analysis and correlation analysis (SCGAT) is proposed to achieve bearing fault diagnosis under imbalanced-dataset conditions. Firstly, a graph attention convolutional neural network is constructed to effectively extract fault-related features from multi-sensor data. Then, a sensor sensitivity analysis module is built to filter and select effective sensor information. A sensor correlation analysis module is introduced to distinguish the correlation between different sensors, and strongly correlated sensors are merged. Finally, the merged features are input into a classifier for fault diagnosis. The effectiveness of the proposed method is verified on a power transmission simulation experiment platform. The experimental results show that the proposed SCGAT can effectively achieve fault diagnosis under imbalanced data conditions, and exhibits higher diagnostic accuracy and stability compared to other models.

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