Brain-Inspired Meta-Learning for Few-Shot Bearing Fault Diagnosis.

Area of Science:

• Mechanical Engineering
• Artificial Intelligence
• Computational Neuroscience

Background:

• Deep learning excels in bearing fault diagnosis but struggles with limited data in industrial settings.
• Overfitting is a major challenge for standard deep learning with few samples.
• Biological brains demonstrate adaptability in learning from scarce data.

Purpose of the Study:

• To develop a novel brain-inspired meta-learning (BIML) strategy for few-shot bearing fault diagnosis.
• To address the limitations of data-driven deep learning in real-world industrial scenarios.

Main Methods:

• Designed a brain-like learning algorithm for spiking neural networks (SNNs) inspired by biological neural mechanisms.
• Integrated a meta-learning strategy to apply the SNN algorithm to few-shot bearing fault diagnosis.
• Conducted theoretical analysis and experimental validation of the BIML strategy.

Main Results:

• The proposed BIML strategy demonstrated superior performance compared to existing few-shot bearing fault diagnosis methods.
• Experimental results validated the effectiveness of the BIML approach in handling limited sample data.
• Theoretical analysis confirmed the efficacy of the BIML strategy.

Conclusions:

• Brain-inspired meta-learning offers a promising solution for challenging few-shot bearing fault diagnosis tasks.
• The BIML strategy effectively overcomes the overfitting issues associated with standard deep learning in low-data regimes.
• This approach enhances the practical applicability of AI in industrial machinery diagnostics.