A meta-learning method for few-shot bearing fault diagnosis under variable working conditions

Intelligent fault diagnosis in various industrial applications has rapidly evolved due to the recent advancements in data-driven techniques. However, the scarcity of fault data and a wide range of working conditions pose significant challenges for existing diagnostic algorithms. This study introduces a meta-learning method tailored for the classification of motor rolling bearing faults, addressing the challenges of limited data and diverse conditions. In this approach, a deep residual shrinkage network is employed to extract salient features from bearing vibration signals. These features are then analyzed in terms of their proximity to established fault prototypes, enabling precise fault categorization. Moreover, the model's generalization in few-shot scenarios is enhanced through the incorporation of a meta-learning paradigm during training. The approach is evaluated using two well-known public bearing datasets, focusing on varying speeds, loads, and high noise environments. The experimental results indicate the superior diagnostic accuracy and robustness of our method compared with those of existing studies.