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Unbalanced Fault Diagnosis Based on an Invariant Temporal-Spatial Attention Fusion Network.

Jianhua Liu¹, Haonan Yang², Jing He²

¹College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China.

Computational Intelligence and Neuroscience

|April 11, 2022

Summary

This study introduces a novel deep learning network for mechanical bearing fault diagnosis, effectively addressing data imbalance issues. The invariant temporal-spatial attention fusion network (ITSA-FN) enhances equipment safety monitoring.

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Area of Science:

Engineering
Artificial Intelligence
Machine Learning

Background:

Mechanical bearing health is critical for equipment safety and operational reliability.
Current deep learning methods for bearing fault diagnosis struggle with imbalanced datasets, limiting their practical application.
Severe class imbalance in fault samples hinders the effectiveness of conventional deep learning models.

Purpose of the Study:

To propose a novel deep learning model for accurate mechanical bearing fault diagnosis under imbalanced data conditions.
To develop an invariant temporal-spatial attention fusion network (ITSA-FN) capable of handling severe class imbalance.
To enhance the safety and reliability of equipment through improved bearing health monitoring.

Main Methods:

Utilized an invariant temporal-spatial attention representation section comprising a pretrained convolutional auto-encoder, a convolutional block attention module, and a long short-term memory network.
Extracted independent and invariant spatial-temporal features from input signals.
Employed a multilayer perceptron for feature fusion and inference, coupled with a novel focal loss function for network training.

Main Results:

The proposed ITSA-FN model demonstrated significant effectiveness in bearing fault diagnosis under unbalanced conditions.
Comparative experiments, ablation studies, and generalization performance tests validated the model's robustness and accuracy.
The method successfully addressed the challenge of severe class unbalancing in bearing fault datasets.

Conclusions:

The invariant temporal-spatial attention fusion network (ITSA-FN) offers a robust solution for bearing fault diagnosis with imbalanced data.
The developed approach enhances the reliability of mechanical bearing health monitoring systems.
This work contributes to improving equipment safety and reducing maintenance costs through advanced AI techniques.