A new meta-transfer learning method with freezing operation for few-shot bearing fault diagnosis. (1st July 2023)

• Record Type: Journal Article
• Authors: Wang, Peiqi; Li, Jingde; Wang, Shubei; Zhang, Fusheng; Shi, Juanjuan; Shen, Changqing
• Is Part Of: Measurement science & technology. Volume 34:Number 7(2023)
• Publication Date: 2023-07-01
• Subjects: meta-learning -- fault diagnosis -- few-shots -- overfit; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/acc67b
• Languages: English
• ISSNs: 0957-0233

Abstract

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.