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日期：2025-06-16
来源：自动化学院
题目：可解释性数据驱动故障诊断
主讲：陈宏田 副教授
时间：6月19日14:00
地点：工程实践中心4-302
主办：自动化学院
主讲嘉宾简介：
陈宏田，现为上海交通大学副教授、博士生导师，国家级高层次青年人才、玛丽居里学者、上海市优才揽蓄人才、上海市高层次人才、浦江学者。本硕毕业于南师大，博士毕业于南京航空航天大学。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、可解释人工智能等及其在高速列车、机器人、海陆空系统应用。目前为止，发表英文专著2部，Automatica与IEEE汇刊70余篇、授权与受理国家专利20余项。主持国际项目、国家级项目等10余项、金额超1000W。获得中国自动化学会优秀博士论文奖、工信部创新特等奖， IEEE RCAE青年科学家奖等多项个人奖与团体奖。目前为IEEE Transactions on Instrumentation and Measurement、IEEE Transactions on Industrial Informatics、IEEE Transactions on Fuzzy Systems、Control Engineering Practice等多个国际期刊编委。受邀作为组织主席，举办RCAE 2022-2025国际会议；并承担多个大会程序主席、联合主席等；同时为可靠性系统科学与工程专委会的副主任委员。
报告主要内容：
The increased complexity and intelligence of automation systems require the development of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected space, this study develops explainable data-driven IFD approaches for nonlinear dynamic systems. More in detail, we parameterize nonlinear systems through a generalized kernel representation used for system modeling and the associated fault diagnosis. An important result obtained is a unified form of kernel representations, applicable to both unsupervised and supervised learning. More importantly, through a rigorous theoretical analysis we discover the existence of a bridge (i.e., a bijective mapping) between some supervised and unsupervised learning-based entities. Notably, the designed IFD approaches achieve the same performance by the use of this bridge. In order to have a better understanding of the results obtained, unsupervised and supervised neural networks are chosen as the learning tools to identify generalized kernel representations and design the IFD schemes; an invertible neural network is then employed to build the bridge between them. This report is a perspective talk, whose contribution lies in proposing and detailing the fundamental concepts for explainable intelligent learning methods, contributing to system modeling and data-driven IFD designs for nonlinear dynamic systems.
