报告人：Kay Chen Tan 教授
Kay Chen Tan is a full Professor with the Department of Computer Science, City University of Hong Kong, Hong Kong. He is the Editor-in-Chief of IEEE Transactions on Evolutionary Computation, was the EiC of IEEE Computational Intelligence Magazine (2010-2013), and currently serves on the Editorial Board member of 15+ international journals. He is an elected member of IEEE CIS AdCom (2017-2019) and is an IEEE Distinguished Lecturer (2015-2017). He has published 200+ refereed articles and 5+ books. He is a Fellow of IEEE.
Abstract: Condition-based maintenance (CBM) is an important tool for running a plant or factory in an optimal manner. Better operations will lead to lower production cost and lower use of resources. Data-driven approaches which do not rely on the domain knowledge are popular in solving CBM problems. This talk will provide an overview of computational intelligence in the application of CBM such as robust prognostic and automated surface inspection. As one of the key enablers of condition-based maintenance, prognostic involves the core task of determining the remaining useful life of a system. This talk will discuss the use of deep learning ensembles to improve the prediction accuracy of remaining useful life estimation. A case study involving the estimation of remaining useful life for turbofan engines will also be presented.