GPPS Hong Kong23: Keynote Speaker
Keynote Title:
Status and Challenges of AI-driven Big Model for Smart Maintenance of Turbomachines
Abstract:
The big model (BM) has recently become a very popular paradigm in different fields due to the emergence of ChatGPT, although it is still too early to assure that the AI-driven BM can be a great revolution in complex industrial fields in terms of the life-cycle management for reliability and efficiency improvement of equipment. This speech attempts to redefine the concept of BM for smart maintenance of turbomachines with the data-physics-VR integration. As key elements in an integrated methodology, the data-driven generative deep learning methods, physics-informed neural networks, and virtual reality-based simulation will be presented to facilitate the implementation of the AI-driven BM for intelligent turbomachines. Recent research and challenges of AI-driven BMs for smart maintenance of turbomachines will be also given through several demonstration examples on gas turbines and wind turbines, with the ultimate purpose of reducing unplanned outages, decreasing the outage time and costs, increasing productivity, and further avoiding the loss of properties and life.
Prof. Xiaomo Jiang
Distinguished Professor of Energy and Power Engineering at Dalian University of Technology and State Key Lab of Structural Analysis, Optimization and CAE Software for Industrial Equipment, and Director of Research Institute for Carbon Neutrality and Provincial Key Lab of Digital Twin for Industrial Equipment at Liaoning. He received his Ph.D. in Structural Engineering from the Department of Civil and Environmental Engineering at The Ohio State University (OSU) in 2005 specializing in intelligent systems, and his MEng in Structural Engineering from the National University of Singapore (NUS) in 2000. From Jan 2005 to Oct 2007, Dr. Jiang worked as a Postdoctoral Researcher at the Department of Civil and Environmental Engineering of Vanderbilt University with a specialty on the predictive analytics and PHM of turbomachines. After then he worked as a Senior Engineer and Technical Leader of AI analytics in Ford Motor, General Electric and Mohawk companies for over 14 years, where he led to drive the AI algorithms development for remote monitoring, analytics, diagnostics, and prognostics of industrial equipment, as well as pilot applications of Predix and Energy IoT in China customers and Qatar LNG plant (Second largest one in the world).
Dr. Jiang’s current research interests include digital twin, AI4Science, and predictive analytics for smart maintenance of turbomachines. He has authored and co-authored more than 100 scientific papers including one book and five book chapters in the cross-disciplinary fields of predictive AI and statistics with engineering and had over 20 Europe, US, and China patents. He has been invited to deliver over 20 keynote speeches and serve as an editorial board or associate editor for more than 50 different international conferences or journals related to smart maintenance and digital twin of turbomachines. Dr. Jiang was an elected member of the ASME VVUQ subcommittee for eight years. He currently is a Chartered Professional Engineer in Structural Engineering in the USA, and a member of specification committee for the Alliance of Chinese International Turbomachinery, Dalian United Research Center of Intelligent Bearing and DLUT AI Research Institute, and the technical committee for Liaoning Innovation Research Institute of Marine Renewable Energy Technology Professional Platform. He has been listed as one of the highly cited Chinese researchers in Mechanical Engineering by Elsevier since 2015, and one of top 2% scientists in the world by Stanford University since 2018.
Prof. Xiaomo Jiang
Energy and Power Engineering at Dalian University of Technology and State Key Lab of Structural Analysis
