报告题目：控制理论辅助机器学习的复杂动态系统故障诊断
报告时间：2025年4月30日（星期三）下午15:30开始
报告地点：中南大学岳麓山校区（校本部）民主楼210会议室
报告人：李琳琳教授，北京科技大学
报告摘要：
Data-driven and machine learning (ML) methods build the mainstream in engineering diagnosis research in recent years. ML-based algorithms focus on feature generation in the data space and on system modelling by means of, technically speaking, identification and optimisation algorithms, known as training and learning as well. In particular, integration of various metrics known in statistics and optimisation theory in the training/learning process makes data-driven and ML-based algorithms considerably capable for dealing with fault diagnosis issues as classification problems. On the other hand, the model-based diagnosis framework is established on the basis of system input-output models and rigorous application of control-theoretic methods. They are efficient and reliable for engineering diagnosis in dynamic control systems. In this talk, the recent attempt to establish a uniform control-theoretic framework is introduced for fault diagnosis in dynamic control systems. The objective of establishing such a framework is twofold. On the one hand, it enables the application of ML-methods to the design of model-based diagnosis systems. On the other hand, application of data-driven and ML-algorithms to diagnosis in industrial automatic control systems will be control-theoretically guided and explained.
报告人简介：
李琳琳，北京科技大学自动化学院教授，博导。2015年获德国杜伊斯堡-埃森大学博士学位。主要研究方向包括：模型与数据驱动的故障诊断与容错控制、面向性能的过程监测、控制理论辅助机器学习的智能诊断与控制等。在Springer发表学术专著一部，在IEEE系列汇刊、Automatica等控制领域权威期刊发表SCI检索论文70余篇。申请或授权发明专利10项，获批钢铁工业协会团体标准1项。主持包括国家自然科学基金优青项目在内的国家级项目4项、其他省部级项目5项。担任ISA Transactions等SCI期刊的编委。
