Novel meta-learning for few-shot bearing fault diagnosis under varying working conditions
Year: 2024
Type: article
Abstract: In practical engineering, large amount data and variable working conditions poses a challenge to most existing Deep Learning(DL) methods. To solve this problem, this paper proposes a new meta-learning approach. Under the condition of limited data, the fault diagnosis under variable working conditions is regarded as a problem with fewer lenses, and the fault diagnosis of few samples across working scenes is carried out based on the Model-Agnostic Meta-Learning(MAML). Gradient-by-gradient rules are used for parameter optimization to achieve an efficient representation of these tasks. Then, the attention mechanism is applied to improve the efficiency of the training. Finally, experiments verified the fault diagnosis accuracy under various working conditions.
Source: Engineering Research Express
Authors: Chuanhao Wang, Jigang Peng, Yongjian Sun
Institution: University of Jinan
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,285
Awards: G3880373652, G4269531113
OpenAlex ID: W4402433208