Prof Wenxian Yang, Newcastle University, United Kingdom
Wind turbine condition monitoring – Challenges, Issues and potential solutions
Prof Wenxian Yang is currently an associate professor in offshore renewable energy at Newcastle University, United Kingdom. Before he joined Newcastle University in 2013, he worked for the UK Offshore Renewable Energy CATAPULT Centre (ORE-CATAPULT) as a technology specialist in wind power.
With expertise in machine condition monitoring and fault diagnosis, signal processing, marine and offshore renewable energy, Dr Yang has been consistently striving to lower the leverage cost of offshore renewable power by developing various approaches using the knowledge in multiple disciplines, e.g. (1) increasing availability and reducing operation and maintenance cost of offshore wind turbines by developing advanced condition monitoring techniques; (2) assuring the safety of the fixed foundation of offshore wind turbines by designing and developing countermeasure devices against scour caused by tidal current; (3) improving the power generation efficiency of wind and tidal turbine by developing biomimetic airfoil/hydrofoil technologies; (4) increasing the economic return of offshore floating wind turbines by developing motion-stable floating platform technologies, etc. Dr Yang is the Associate Editor of IET Renewable Power Generation and the Associate Editor of Frontier in Mechanical Engineering. Besides, he is a Chartered Engineer and also the editorial member of the other 11 international journals, such as Electronics, Frontiers Journal of Renewable Energy, etc. In 2017, his research on the ageing issues of wind turbine components and assemblies was identified by Renewable Energy Global Innovation as a key contribution to the excellence in renewable and clean energy research.
Prof Surjya K Pal, IIT Kharagpur, India
Artificial Intelligence in Industrial Research
Surjya K Pal is the Lord Kumar Bhattacharyya Chair Professor in Manufacturing in the Department of Mechanical Engineering at IIT Kharagpur. He is the Founder Chairperson of the Centre of Excellence in Advanced Manufacturing Technology; a Centre focused to solving the industrial problems.
He has published 286 research articles, including, 176 International and National Journal Papers, 15 International Book Chapters, and 89 Conference papers. He has filed 12 patents. His innovation, “Low-cost AI solution for metrological inspection,” is selected in the top 3 by INDIAai Lab2Market (The National AI Portal of India, a MeitY, NeGD, and NASSCOM initiative). He has two patents jointly with TATA Consultancy Services. He is the author of the textbook, “Digital Twin – Fundamental Concepts to Applications in Advanced Manufacturing.” Under his esteemed supervision, 20 scholars have completed their doctorate degrees, and 16 are continuing. Prof. Pal has executed a huge number of projects worth over INR 85 Cr. He was also the Associate Dean of Alumni Affairs and Branding at IIT Kharagpur.
Prof Weihua Li, South China University of Technology, China
Deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications, and challenges
Weihua Li, Senior Member, IEEE, received the Ph.D. degree in mechanical engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2003. He is currently a Deputy Dean and Professor with the Shien-Ming Wu School of Intelligent Engineering, Guangzhou International Campus, South China University of Technology, Guangzhou, China. Prof. Li is now serving as the co-chair of Technical Committee (TC-3) on Condition Monitoring & Fault Diagnosis Instrument, IEEE Instrumentation and Measurement Society (IEEE IM Society). He serves as the member of the Editorial Board of several journals, such as IEEE Transactions on Instrumentation and Measurement, IEEE Sensors Journal, Chinese Journal of Mechanical Engineering (CJME), Journal of Dynamics, Monitoring and Diagnostics (JDMD), and Journal of Vibration Engineering (JVE).
Prof Li’s research interests include Industrial intelligence, Industrial Big Data, Digital Twins, Intelligent Maintenance, and Intelligent Connected Vehicles. He is the PI (principal investigator) of over 20 projects which are funded by National Natural Science Foundation of China, National Key Research and Development Program of China, Key Research and Development Program of Guangdong Province, University-Industry Cooperation, etc. Prof. Li has published over 100 papers in related journals, including IEEE Trans. on Industrial Informatics, Instrumentation & Measurement, IEEE/ASME Trans. on Mechatronics, IEEE Sensor Journal, Renewable Energy, Mechanical System & Signal Processing, Journal of Mechanical Engineering, etc. In addition, he has published 4 books and issued more than 20 Chinese invention patents.
Dr. Xiang Li, Xi’an Jiaotong University, China
Data privacy-preserving collaborative intelligent machinery fault diagnosis
Dr. Xiang Li is an associate professor at School of Mechanical Engineering, Xi’an Jiaotong University, China. Prior to joining Xi’an Jiaotong University, he was a postdoctoral fellow at University of Cincinnati, US, and a visiting scholar at University of California at Merced. He obtained both his PhD and BS degrees in Tianjin University, China. His research interests include industrial artificial intelligence, industrial big data, intelligent machine maintenance, fault diagnosis and prognosis. He is the P.I. of one NSFC-funded project and three other research projects.
Dr. Xiang Li has published more than 30 high-level journal papers, including IEEE Transactions on Industrial Electronics, IEEE Transactions on Industrial Informatics etc. He has published 13 ESI highly cited papers and 4 ESI hot papers. His citations in Google Scholar are beyond 3000 with an H-index of 24. His research works have been successfully applied in the real industries such as intelligent manufacturing. He is the editorial board member of many journals including IEEE/CAA Journal of Automatica Sinica etc.
Dr. Guojin Feng, Hebei University of Technology, China
On-Rotor Sensing Technology for Machinery Fault diagnosis
Dr. Guojin Feng is an associate professor at the School of Mechanical Engineering, Hebei University of Technology. He received his PhD from the University of Huddersfield. Prior to joining Hebei University of Technology, he worked as a Senior Research Fellow at the University of Huddersfield for 2 years and Research Fellow at Brunel University for 3 years. His research interests include: industrial low-power wireless sensor networks, mechanical fault feature extraction and fault identification, on-rotor sensing technology, energy harvesting from rotating machinery, intelligent fault diagnosis based on machine/deep learning, etc.
He has published more than 30 high-level journal papers, including Mechanical Systems and Signal Processing, Measurement, IEEE Transactions on Instrumentation and Measurement etc. He is the reviewer for over 30 SCI journals. Dr. Guojin worked as the technical leader in one EU Horizon 2020 project, five Innovation UK projects and one international cooperation project with University of Malaya. He a member of the Institute of Electrical and Electronic Engineers (IEEE) and the Institution of Engineering and Technology (IET).
Prof Chao Fu, Northwestern Polytechnical University, China
Dynamics of faulted rotor-bearing systems and uncertainty quantification
Dr Chao Fu is a professor and PhD supervisor in Mechanics in the Department of Engineering Mechanics at Northwestern Polytechnical University, China. He graduated from Northwestern Polytechnical University in 2018 with a PhD degree in Mechanical Engineering, after which he stayed in CEPE group, Huddersfield for over two years as a research fellow. From July 2021, he has been with Northwestern Polytechnical University.
Prof Chao Fu has published over 40 research papers, including 28 journal articles and over 10 conference proceedings. Some of his papers receive peer recognitions and become ESI highly cited papers. His research area mainly involves linear and nonlinear vibrations of rotating machines, dynamics with faults and uncertainty quantification. Chao Fu also reviews for over 30 scientific international journals in his field. He is the youth editorial board of Chinese Journal of Applied Mechanics. He has been awarded several research grants from different funders, including Shaanxi Provincial Natural Science foundation and Shanghai Sailing Program for Young Talents.
Dr. Yongjian Ji, Beijing Information Science & Technology University, China
Robotic Milling Chatter Stability Prediction
Dr Yongjian Ji, received his Ph.D. degree in Mechanical Engineering from Beijing Institute of Technology, China. He is currently an associate professor at Key Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing Information Science & Technology University, China. His research interests include operation condition analysis of industrial robot, manufacturing process monitoring, and fault diagnosis techniques of High-end CNC equipment.
Dr. Yongjian Ji has published more than 20 research papers and one book in which systematically introduced the research results of milling stability prediction and chatter suppression techniques. He has won the 10th Hiwin Doctoral Dissertation Award in Mechanical Engineering. He is a senior member of the Chinese Mechanical Engineering Society. As the project leader, he has been awarded several research grants from the National Natural Science Foundation of China and other funders. Some of his research works have been successfully applied in the field of precision manufacturing and intelligence operations.
