Meta-learning for few-shot bearing fault diagnosis under complex working conditions

Deep learning-based bearing fault diagnosis has been systematically studied in recent years. However, the success of most of these methods relies heavily on massive labeled data, which is not always available in real production environments. Training a robust bearing fault diagnosis model with limited data and working well under complex working conditions remains a challenge. In this paper, a novel meta-learning fault diagnosis method (MLFD) based on model-agnostic meta-learning is proposed to address this issue. The raw signals of different working conditions are first converted to time–frequency images and then randomly sampled to form tasks for MLFD according to the protocol of meta-learning. The MLFD model acquires prior knowledge by optimizing initialization parameters based on multiple fault classification tasks of known working conditions during the meta-training process, and achieves fast and accurate few-shot bearing fault diagnosis under unseen working conditions by leveraging the learned knowledge. To comprehensively evaluate the performance of our method, a series of experiments were conducted to simulate different industrial scenarios based on the Case Western Reserve University Bearing Fault Benchmark, and the results demonstrate the superiority of MLFD in solving the few-shot fault classification problem under complex working conditions.