Work

Improved metric-based meta learning with attention mechanism for few-shot cross-domain train bearing fault diagnosis

Year: 2024

Type: article

Abstract: Traction motor bearings, as a crucial component of subway trains, play a pivotal role in ensuring the safety of train operations. Therefore, intelligent diagnosis of train bearings holds significant importance. However, due to the complex and dynamic nature of bearing conditions coupled with limited fault data availability, traditional diagnostic methods fail to yield satisfactory results. To address this issue, we propose an improved metrics-based meta-learning approach for accurate few-shot cross-domain fault diagnosis of train bearings. Firstly, we introduce a 1D-signal channel attention mechanism that effectively extracts latent features and enhances recognition accuracy. Secondly, by incorporating the Adabound algorithm into our model framework, we further enhance its classification performance. Finally, through several case studies, we demonstrate the effectiveness of our proposed method in comparison to other approaches within similar settings.

Source: Measurement Science and Technology

DOI: 10.1088/1361-6501/ad30b6

Authors: Hao Zhong, Deqiang He, Zhenpeng Lao, Zhenzhen Jin, Guoqiang Shen, Yanjun Chen

Institutions: Guangxi University, Guangxi Key Lab of Manufacturing System and Advanced Manufacturing Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

Funders: National Natural Science Foundation of China, Guangxi Key Lab of Manufacturing System and Advanced Manufacturing Technology, Division of Graduate Education