报告题目：Learning-based Fault Diagnosis for Rotating Machinery

报告时间：2023年4月8日9:30

报告地点：线下-信息学馆301会议室

报告人：Hamid Reza Karimi 教授

邀请人：杨东升 教授

报告人简介：

Hamid Reza Karimi is currently Professor of Applied Mechanics with Department of Mechanical Engineering, Politecnico di Milano, Italy. Prof. Karimi’s original research are within control systems, vibrational systems, AI, Mechatronics with applications to vehicles, robotics. He is a Member of Academia Europa, Distinguished Fellow and Executive Director of the International Institute of Acoustics and Vibration, Fellow of The International Society for Condition Monitoring, Fellow of The Asia-Pacific Artificial Intelligence Association, Member of Agder Academy of Science and Letters and a member of IFAC Technical Committees on Mechatronic Systems, Robust Control, and Automotive Control. He is the recipient of the 2021 CM Innovation Award, Web of Science Highly Cited Researcher in Engineering, 2020 IEEE TCAS Guillemin-Cauer Best Paper Award, August-Wilhelm-Scheer Professorship Award, JSPS Research Award, and Alexander-von-Humboldt-Stiftung research Award. Karimi is currently serving as the Editor-in-Chief, Associate Editor or Book Series Editor. He has also participated as General Chair, keynote speaker or program chair for several international conferences in the areas of Control Systems, Robotics and Mechatronics

报告内容：

Industry 4.0 has enabled the automation of process improvements and decision making based on the collection of large amounts of plant data. Due to the economic advantages of maintenance optimization, there is significant interest from both academia and industry on the topic of fault detection and prognostics for complex systems. The objective of this speech is to address some challenges and recent results on fault diagnosis of mechanical systems, with a focus on advanced artificial intelligence algorithms developments. Specifically, different deep learning models such as deep supervised, unsupervised and reinforcement learning algorithms are examined to establish a trustworthy intelligence fault diagnosis model. The talk will be concluded with some concluding remarks on both technical and practical aspects of intelligent fault diagnosis in identifying the fault types in rotary machines.