A new meta-transfer learning method with freezing operation for few-shot bearing fault diagnosis

Abstract Deep learning for bearing fault diagnosis often requires a large quantity of comprehensive data to give support in the field of rotating machinery fault diagnosis. However, large-quantity datasets for model training are difficult to obtain in actual working environments. Therefore, bearing fault diagnosis problems under practical working conditions are often considered few-shot problems. Meta-learning can be adopted to solve these few-shot problems. Traditional meta-learning methods, however, can lead to model overfitting, and shallow neural networks are usually used to avoid overfitting. As a result, the features extracted by the shallow neural network are insufficiently rich to exploit the optimal performance of the model. A few-shot fault diagnosis method based on meta-learning, named meta-transfer learning with freezing operation (MTLFO), is proposed in this study to solve these problems. MTLFO can learn new knowledge rapidly through a small number of samples. The hyperparameter self-regulation ability of meta-learning is adopted by MTLFO, and a freezing operation is used to deal with the neuronal nature of meta-learning to ensure that the neurons from different tasks are transferred by utilizing scaling and shifting. MTLFO avoids the overfitting problem in traditional meta-learning methods and presents more advantages in solving few-shot problems in fault diagnosis compared with other types of methods.