Cross-Domain Fault Diagnosis Using Knowledge Transfer Strategy: A Review

IEEE Access

Jiancheng Yin¹  Huailiang Zheng¹  Rixin Wang¹  Yuantao Yang¹  Minqiang Xu¹  Yongbo Li²  Yuqing Li³ 

[1] Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin, China;MIIT Key Laboratory of Dynamics and Control of Complex Systems, Northwestern Polytechnical University, Xi&x2019;an, China;

关键词: Cross-domain;    domain adaptation;    fault diagnosis;    review;    transfer learning;   

DOI  :  10.1109/ACCESS.2019.2939876

来源: DOAJ

【 摘 要 】

Data-driven fault diagnosis has been a hot topic in recent years with the development of machine learning techniques. However, the prerequisite that the training data and the test data should follow an identical distribution prevents the conventional data-driven diagnosis methods from being applied to the engineering diagnosis problems. To tackle this dilemma, cross-domain fault diagnosis using knowledge transfer strategy is becoming popular in the past five years. The diagnosis methods based on transfer learning aim to build models that can perform well on target tasks by leveraging knowledge from semantic related but distribution different source domains. This paper for the first time summarizes the state-of-art cross-domain fault diagnosis research works. The literatures are introduced from three different viewpoints: research motivations, cross-domain strategies, and application objects. In addition, the corresponding open-source fault datasets and several future directions are also presented. The survey provides readers a framework for better understanding and identifying the research status, challenges and future directions of cross-domain fault diagnosis.

【 授权许可】

Unknown