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Related Experiment Video

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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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An Imbalanced Fault Diagnosis Method Based on Multi-Sensor Selection and Graph Attention Mechanism.

Qiangqiang Xiong1, Qiming Shu2, Ke Wu3,4

  • 1Jiangxi Key Laboratory of Modern Agricultural Equipment Jiangxi Province, College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

A novel approach using a graph attention convolutional neural network (SCGAT) effectively diagnoses bearing faults, even with imbalanced data. This method enhances diagnostic accuracy and stability in critical machinery monitoring.

Keywords:
bearingfault diagnosisgraph attention networkimbalanced dataset

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Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Data Science

Background:

  • Bearing diagnostic errors are common due to imbalanced datasets (normal vs. fault data).
  • Existing methods struggle with the significant data imbalance in bearing fault detection.
  • Accurate fault diagnosis is crucial for preventing machinery failure and ensuring operational safety.

Purpose of the Study:

  • To propose a novel method for effective bearing fault diagnosis under imbalanced dataset conditions.
  • To enhance the accuracy and stability of fault diagnosis by addressing data imbalance.
  • To introduce a robust feature extraction and selection mechanism for multi-sensor bearing data.

Main Methods:

  • A graph attention convolutional neural network (SCGAT) was developed for feature extraction from multi-sensor data.
  • Sensor sensitivity analysis was employed to filter and select relevant sensor information.
  • Sensor correlation analysis was used to merge highly correlated sensor data, reducing redundancy.
  • The integrated features were then fed into a classifier for final fault diagnosis.

Main Results:

  • The SCGAT method demonstrated effective bearing fault diagnosis capabilities even with imbalanced datasets.
  • Experimental validation on a power transmission simulation platform confirmed the method's performance.
  • The proposed SCGAT model achieved higher diagnostic accuracy and superior stability compared to existing models.
  • The sensitivity and correlation analysis modules successfully refined sensor data for improved diagnosis.

Conclusions:

  • The SCGAT method offers a robust solution for bearing fault diagnosis in imbalanced data scenarios.
  • The integration of sensitivity and correlation analysis enhances feature representation for accurate fault detection.
  • This approach provides a significant advancement in condition monitoring and predictive maintenance for rotating machinery.
  • The SCGAT model shows promise for real-world applications requiring reliable fault diagnosis under challenging data conditions.