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MR-FuSN: A Multi-Resolution Selective Fusion Approach for Bearing Fault Diagnosis.

Lin Sha¹, Shikai Tang¹, Min Wang²

¹School of Control Science and Engineering, Tiangong University, No. 399 Binshui West Road, Tianjin 300387, China.

Sensors (Basel, Switzerland)

|February 26, 2025

Summary

This summary is machine-generated.

This study introduces the Multi-Resolution Fusion Selection Network (MR-FuSN) for robust bearing fault diagnosis. The novel network excels in noisy industrial settings, achieving high diagnostic accuracy even with significant signal interference.

Keywords:

deep learning fault diagnosis multi-resolution selective networks vibration signal

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Area of Science:

Mechanical Engineering
Signal Processing
Artificial Intelligence

Background:

Vibration signals are crucial for bearing fault diagnosis.
Industrial environments introduce noise, challenging accurate fault detection.
Existing methods struggle with noise interference, impacting diagnostic reliability.

Purpose of the Study:

To propose a novel network architecture for enhanced bearing fault diagnosis.
To improve robustness against environmental noise in vibration signals.
To achieve high diagnostic accuracy in challenging industrial conditions.

Main Methods:

Developed the Multi-Resolution Fusion Selection Network (MR-FuSN).
Employed multi-resolution feature extraction to capture diverse signal characteristics.
Integrated an adaptive kernel convolution strategy for noise resilience.

Main Results:

MR-FuSN demonstrated outstanding performance in noisy environments (-5 dB to 10 dB SNR).
Achieved a diagnostic accuracy of 99.97% under 0 dB signal-to-noise ratio conditions.
Successfully extracted domain-invariant features, enhancing diagnostic robustness.

Conclusions:

The MR-FuSN offers a robust solution for bearing fault diagnosis in industrial settings.
The proposed network architecture effectively mitigates noise interference.
This research provides valuable technical support for practical fault diagnosis applications.