文献类型：期刊文献

英文题名：MDATS―A Multi-Domain Adaptive Classification Strategy for Mechanical Fault Detection

作者：Wang, Zesen[1]; Huang, Wenchao[1]; Hu, Yue[2]; Gao, Yang[2]

机构：[1] East China University of Science and Technology, Shanghai, China; [2] School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China

年份：2024

起止页码：405

外文期刊名：2024 10th Asia Conference on Mechanical Engineering and Aerospace Engineering, MEAE 2024

收录：EI(收录号：20253118907865)

语种：英文

外文关键词：Classification (of information) - Fault detection - Time series

摘要：Mechanical fault detection and classification play a critical role in maintaining the reliability and safety of industrial systems. This paper presents a novel multi-domain adaptive classification strategy (MDATS) designed to enhance fault detection accuracy in varying operational conditions. Using time series data from the XJTU-SY bearing datasets, our approach leverages TS2Vec for time series encoding, coupled with CNN, TCN, and Transformer architectures to extract multi-scale features. To address domain adaptation challenges, the Transferable Domain Adaptation Network (TDAN) is employed, improving model generalization across multiple datasets. Experimental results demonstrate that the proposed MDATS framework significantly improves fault classification performance, particularly in target domains with differing characteristics. The framework is evaluated against baseline models, achieving superior results in both accuracy and domain adaptation. ? 2024 IEEE.