To address the issue of model-agnostic meta-learning networks incurring overfitting due to high model complexity during training in small-sample conditions

we propose a first-order meta-learning-based rolling bearing fault diagnosis model. Initially

we form meta-tasks by randomly sampling original signals following a meta-learning strategy. Subsequently

within meta-tasks associated with known working conditions

we employ a wide-kernel convolutional network to enhance the model′s capability to extract fault information from one-dimensional vibration signals

acquiring meta-knowledge. Additionally

to reduce model training complexity and mitigate overfitting

we employ a gradient-based model optimization

progressively adjusting from initial parameters to optimal training weights. Finally

leveraging the acquired meta-knowledge

our approach can realize rapid and accurate fault classification under unknown working conditions. The experimental results show that the proposed method achieves the highest diagnostic accuracy on both validations bearing datasets

proving the effectiveness and superiority of the proposed method.