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

Deep learning has attracted much attention in bearing fault diagnosis because of its high precision and end-to-end modules. However, in real industrial scenarios, some complex mechanical structures and working environments hinder data collection and fault reproduction, which makes bearing fault diagnosis with few samples a practical but challenging issue. As a data-driven approach, the standard deep learning method cannot extract features from a few samples due to overfitting. Neuroscience research has shown that the learning mechanism of the biological brain is more adaptable to learning tasks with few samples. Motivated by this, we propose a brain-inspired meta-learning (BIML) strategy for diagnosing few-shot bearing faults. Specifically, we design a brain-like learning algorithm for spiking neural networks (SNNs) based on the biological nervous system's learning mechanism and introduce a meta-learning strategy to apply it to the fault diagnosis task of bearing with few samples. Experimental results show that BIML is better than existing few-shot bearing fault diagnosis methods. Subsequently, we conduct a theoretical analysis of the effectiveness of BIML strategies and verify our analysis through experiments.