An improved prototypical network with L2 prototype correction for few-shot cross-domain fault diagnosis

Deep learning has made great achievements in fault diagnosis research. However, due to the changeable working conditions and lack of data in the current industrial scene, it is challenging to apply fault diagnosis methods based on deep learning in practical industry scenarios. To address this issue, a l2 prototype correction network (LPCN) with coordinate attention (CA) is proposed. CA is introduced into the feature extractor, cooperating with the proposed time-frequency representation based on continuous wavelet transform (CWT), which could provide prototypical network better feature maps for classification. Additionally, l2 prototype correction is proposed to mitigate the length fluctuations caused by the domain shift and enable LPCN to find more accurate prototypes, thus further improving the generalization performance of LPCN. The effectiveness of the proposed method is validated on two bearing vibration datasets, showing that it not only achieves higher classification accuracy but also requires less data. As a key issue in meta learning, the construction of the meta-training dataset in fault diagnosis is also discussed to guide application of meta-learning in the field of fault diagnosis.