Few-shot learning fault diagnosis of rolling bearings based on siamese network

This paper focuses on the fault diagnosis problem in the scenario of scarce bearing samples, facing two main challenges: complex noise background and variations in operating conditions. While deep learning-based fault diagnosis methods have achieved significant progress, they heavily rely on large amounts of samples. This paper proposes a few-shot learning fault diagnosis method based on siamese networks (SN), which classify samples based on the similarity between pairs rather than end-to-end classification. Tested on two bearing datasets, the proposed method outperforms SVM, DCNN, WDCNN, and CNN-BiGRU. The influence of factors such as parameter regularization, noise, and load variation on the proposed method is also discussed. Experimental results demonstrate that double parameter regularization contributes more to the model’s generalization ability, maintaining good stability and generalization even under noise interference or load variation.