报告题目：Transfer Learning-motivated Intelligent Fault Diagnosis Designs: A Survey, Insights, and Perspectives
报告摘要：Over the last decade, transfer learning has attracted a great deal of attention as a new learning paradigm, based on which fault diagnosis (FD) approaches have been intensively developed to improve the safety and reliability of modern automation systems. Because of inevitable factors such as the varying work environment, performance degradation of components, and heterogeneity among similar automation systems, the FD method having long-term applicabilities becomes attractive. Motivated by these facts, transfer learning has been an indispensable tool that endows the FD methods with self-learning and adaptive abilities. On the presentation of basic knowledge in this field, a comprehensive review of transfer learning-motivated FD methods, whose two sub-classes are developed based on knowledge calibration and knowledge compromise, is carried out in this report. Finally, some open problems, potential research directions, and conclusions are highlighted. Different from the existing reviews of transfer learning, this survey focuses on how to utilize knowledge specifically for the FD tasks, based on which three principles and a new classification strategy of transfer learning-motivated FD techniques are also presented. We hope this work will constitute a timely contribution to transfer learning-motivated techniques regarding the FD topic.
专家简介：陈宏田，现为上海交通大学长聘教轨副教授，IEEE会员，中国自动化学会会员。本硕毕业于南师大，博士毕业于南京航空航天大学。2018年在德国先进控制与复杂系统研究所做访问学者。2019年至2023年为加拿大Alberta大学博士后。主要研究方向为数据驱动技术、人工智能、量子计算、分布式系统等及其在高速列车、海陆空系统的故障诊断应用。目前为止，发表英文专著2部，国际英文论文IEEE汇刊40余篇、授权与受理国家专利8项。主持、参与国家级和省部级项目6项。获得中国自动化学会优秀博士论文奖、江苏省优秀博士论文奖。目前为IEEE Transactions on Instrumentation and Measurement、IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Artificial Intelligence等国际期刊编委、客座编委。