Seminar on Computational Intelligence for Diagnosis and Prognosis

Date: 26 Feb 2010 - 26 Feb 2010

Venue: SIMTech Auditoriium, Tower Block, Level 3

The main objective of this seminar is to provide participants with a better understanding of the current state of research on computational intelligence for diagnosis and prognosis. Key topics include the "analysis and design of K-winners-take-all networks for neurodynamic optimisation" and "condition monitoring for wind turbine drive trains in Vestas". This seminar is co-organised by IEEE Computational Intelligence Society Singapore Chapter.    

Presentation 1: Analysis and Design of K-Winners-take-all Networks via Neurodynamic Optimisation
Winner-take-all is a general tool commonly used in machine learning and data mining. Over the last twenty years, many K-winners-take-all neural networks and circuits have been designed and analysed with reduced complexity and improved performance. This presentation will discuss the mathematical problem formulations of the K-winners-take-all solutions for neurodynamic optimisation and several K-winners-take-all networks with reducing model complexity, based on neurodynamic optimisation models. Participants will learn more about the simplest model complexity and maximum computational efficiency. The presentation will also show the extensive Maote Carlo simulation results and several applications to demonstrate the computing performance.

Presentation 2: Condition Monitoring for Wind Turbine Drive Trains in Vestas  

Wind power is a natural and virtually inexhaustible source of clean, renewable energy without greenhouse gas emissions. In the last two decades, wind energy has become the leading developing direction in electric power, one of the key strategies to mitigate the threat of global warming. A major challenging issue with wind power system is the relatively high cost associated with wind turbine operation and maintenance since wind turbines are hard to access for inspection and maintenance purposes. There is a high motivation to pursue online condition monitoring and predictive maintenance strategies to ensure uninterrupted power production. This presentation will give a brief introduction of the current practice and experience gained with the remote condition monitoring of a large number of Vestas turbines. 

About the Speakers
Dr Wang Jun is Professor in the Department of Mechanical and Automation Engineering at the Chinese University of Hong Kong (CUHK), a top HK university with strong research emphasis. Prior to joining CUHK, Dr Wang held several academic positions at Dalian University of Technology, Case Western Reserve University, and University of North Dakota. He also held various short-term visiting positions at USAF Armstrong Laboratory (1995), REKEN Brain Science Institute (2001), Universite Catholique de Louvain (2001), Chinese Academy of Sciences (2002), and Huazhong University of Science and Technology (2006–-2007). His current research interests include neural networks and applications. He has published over 140 journal papers, 12 book chapters, 8 edited books, and numerous conference papers pertaining to neural networks and applications.
Currently, he is serving as Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics – Part B and a member of the Editorial Advisory Board of the International Journal of Neural System. 
Dr Wang also served as an Associate Editor of of the IEEE Transactions on Neural Networks (1999-2009) and the IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005) and as guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), and Neurocomputing (2008). An active organiser of several international conferences, including the 2008 IEEE World Congress on Computational Intelligence and was the General Chair of the 13th International Conference on Neural Information Processing (2006). He is an IEEE Fellow and IEEE Distinguished Lecturer. He is former President of the Asia Pacific Neural Network Assembly in 2006. Since 2008, he has held Changjiang Chair Professorship in computer science and engineering at Shanghai Jiao Tong University.

Dr Li Huaizhong is currently Associate Principal Engineer with Vestas Wind Systems. He received his bachelor degree from Tsinghua University in 1988, master degree from Xi’an Jiantong University in 1991 and PhD from the National University of Singapore in 2002, all in mechanical engineering. He has extensive R&D experience both in academia and industry. A former member of the University of New South Wales and researcher in Singapore Institute of Manufacturing Technology, Dr Li has published over 30 peer reviewed technical papers for international journals and conferences. His research interests include vibration analysis and control, condition monitoring and fault diagnosis of machinery, manufacturing technologies and mechatronics. 

2.00pm-2.25pm      Registration
2.25pm-2.30pm      Overview of PHM Research at SIMTech, MEC by Dr Ian Chan
2.30pm-3.30pm      Presentation by Prof Wang Jun, IEEE Distinguished Speaker 
3.30pm-4.00pm      Presentation by Dr Li Huazhong, Vestas Wind Systems
4.00pm-4.30pm      Networking & Refreshments

Who Should Attend
Senior managers, R&D staff, academic staff, students and post-graduate students. 

Registration for this seminar is free of charge. Seats are confirmed on a first-come, first-served basis.  

For technical enquiries, please contact:
Dr Ian Chan, Associate Research Scientist, Email:; Tel: 6793 8995
Professor Er Meng Joo, School of EEE, NTU & Chairman, IEEE CIS Singapore Chapter, Tel: 65138167; Email:

For general enquiries, please contact Alice Koh, Email: