报告题目：A Bridge Between Unsupervised and Supervised Learning-based Fault Diagnosis Approaches
报告摘要：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 study is a perspective article, 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.
个人简历：陈宏田，现为加拿大阿尔伯塔大学博后。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。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牵引系统的故障诊断应用。目前为止，发表英文专著3部，国际英文论文70余篇（IEEE汇刊30篇）、授权与受理国家专利12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）、RCAE大会青年科学家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等国际期刊编委与客座编委。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DDCLS’22大会与DOCS’22大会专题主席。