A Novel Lightweight Relation Network for Cross-Domain Few-Shot Fault Diagnosis

•We propose a modified Ghost block, which has better feature extraction capability while reducing the number of model parameters. Then, a lightweight encoder module built from the Ghost block is proposed to improve the relation network.•A calibration method based on semi-supervised learning is proposed, which can utilize unlabeled data to alleviate the prototype deviation problem caused by limited data and domain shift.•We propose simulating realistic fault diagnosis scenarios by means of few-shot across-domain diagnostic tasks and we also explore ways to better construct meta-tasks in the field of fault diagnosis.