Year: 2024
Type: article
Abstract: Distribution shifts pose challenges for deploying intelligent fault diagnosis models in real-world engineering applications. To address these shifts, research on intelligent fault diagnosis using transfer learning has been actively pursued. Among the unsupervised transfer learning methods, domain adaptation and domain generalization are particularly noteworthy. Domain adaptation requires unlabeled target domain data, while domain generalization eliminates this need, offering greater flexibility and versatility. Since domain generalization was first applied to fault diagnosis in 2020, numerous diagnostic techniques have emerged, necessitating a thorough review. This paper first classifies existing domain generalization fault diagnosis studies in general by task type and further refines them based on methodological theories. Subsequently, it summarizes the core concepts of each theory and reviews related studies in fault diagnosis. Finally, promising future research directions are discussed.
Source: Scientia Sinica Technologica
Authors: HaiDong SHAO, YiMing XIAO, Shen YAN, Jie WANG, Ying PENG
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4403274652