@article{WANG2023, 
author = {Peiqi WANG and Changqing SHEN and Bojian CHEN and Juanjuan SHI and Weiguo HUANG and Zhongkui ZHU},
title = {Generic meta-transfer learning model with special neuronal processing parameters for few-shot fault bearing diagnosis},
year = {2023},
journal = {Journal of Advanced Manufacturing Science and Technology},
volume = {3},
number = {3},
pages = {2023007},
keywords = {Meta-learning, Fault diagnosis, Rotating machinery, Few-shot learning, Overfitting},
url = {https://www.sciopen.com/article/10.51393/j.jamst.2023007},
doi = {10.51393/j.jamst.2023007},
abstract = {The society is now in the data-rich environment, and deep learning is widely used in bearing fault diagnostic technology due to the advancement of information technology. These methods typically need a large amount of data to support. However, in some practical cases, only few of samples are frequently available when a fault occurs rather than adequate data to be analyzed. This situation indicates that bearing fault diagnostic problems are frequently few-shot problems. In this work, a generic meta-transfer learning model with special neuronal processing parameters (MSNPP) is proposed. MSNPP avoids the issue of overfitting commonly encountered in traditional meta-learning approaches when solving few-shot problems and maintains excellent performance when extracting features with deep networks. Moreover, MSNPP discovers the connection between different tasks by analyzing a few samples and quickly adapts to new tasks. In MSNPP, a technique known as neuron transfer (NT) is used to manipulate neurons by scaling and shifting them. The scaling and shifting parameters are used as meta-learning hyperparameters to transfer within different tasks, which is the work of NT. Experimental result shows that MSNPP prevents the issue of overfitting in conventional meta-learning approaches and achieves satisfactory accuracy and robustness when solving few-shot problems in fault diagnosis.}
}