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知行讲坛:Explainable Fault Diagnosis: A Bridge Between Unsupervised and Supervised Learning-based Fault Diagnosis Approaches

发布时间:2023-03-30 点击:

师资职位 副教授 年份 2023
报告时间 3月31日 10:00—11:00 报告地址 工5—404
报告人 陈宏田 职称

讲坛题目:Explainable Fault Diagnosis: A Bridge Between Unsupervised and Supervised Learning-based Fault Diagnosis Approaches

人:陈宏田副教授

讲座时间:2023331日,10:00—11:00

讲座地点:工5—404

主办单位:研究生院

承办单位:电子信息工程学院

摘要: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.

个人简介:

陈宏田,上海交通大学副教授,IEEE会员,中国自动化学会会员。本硕毕业于南京师范大学,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70余篇(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、工信部创新特等奖(全校第一名),群星创新大奖、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 Neural Networks and Learning Systems、IEEE Transactions on Artificial Intelligence等国际期刊编委、客座编委。受邀作为组织主席,举办RCAE 2022国际会议与AMEE 2022国际会议;并承担多个大会程序主席、专题主席等。