A multi scale meta-learning network for cross domain fault diagnosis with limited samples

In recent years, data-driven machine learning models have achieved good results in fault diagnosis of rotating machinery under different working conditions. However, in practical applications, the lack of fault samples under various working conditions makes the training of models difficult. In this paper, a multi scale meta-learning network (MS-MLN) that can be applied to few-shot cross-domain diagnosis of rotating machinery is proposed to address this issue. MS-MLN consists of a multi scale feature encoder, a metric embedding process and a classifier. The model is trained by an episodic metric meta-learning strategy under few-shot and domain shift scenarios. Extensive experiments are carried out to verify the effectiveness of MS-MLN, results show that MS-MLN outperforms most benchmark models in bearing and wind turbine gearbox fault diagnosis. Visualization is applied to the model to study its effectiveness. Ablation study is also conducted to discuss the impact of different parts of the model's feature encoder on its performance in detail.