Draft Full Paper Due:
April 20, 2018 (Extended)

Notification of Draft Paper Acceptance:
May 10, 2018 (Extended)

Author Registration Deadline:
May 25, 2018 (Extended)

Final Manuscript Due:
May 25, 2018 (Extended)

Early Bird Registration Deadline:

May 25, 2018

 

Home > Keynote Speakers
Keynote Speakers

(1)  Machine Health Monitoring and Its Application in the Design Optimization of Mechatronic Systems

Keynote Speaker:
Professor Clarence W. de Silva, P.Eng., Fellow ASME, Fellow IEEE, and Fellow Canadian Academy of Engineering, Fellow Royal Society of Canada
Department of Mechanical Engineering, University of British Columbia, Canada
 

 

Abstract:

Mechatronic systems consist of components from different domains, e.g., mechanical, electrical/electronic, hydraulic, pneumatic, thermal, and control. Due to the system complexity, their design and fault accommodation provide special challenges. For example, continuous or on-demand design improvements should be incorporated rapidly and effectively, which will address new design requirements or resolve existing weaknesses of the original design. With the rapid advances in sensor technologies, wireless communication, data storage, and data mining, machine health monitoring (MHM) has achieved significant capabilities to monitor the performance of an operating machine. The extensive data that may be acquired by the MHM system can be employed in design improvement of the monitored system.  The present talk focuses on this aspect. A systematic and closed-loop framework for continuous design evolution of a mechatronic system with MHM is presented. Possible design weaknesses of the monitored mechatronic system are detected by a systematic approach using the information from MHM. The method incorporates an index to identify a possible design weakness by evaluating the performance, detecting failures and estimating the health status of the system. Deep learning, and particularly deep neural networks (DNNs) may be favorably employed for this purpose. It takes advantages of the massive unlabeled MHM data to learn representative features. Using very few items of labeled data, the DNN-based fault diagnosis approach can achieve superior diagnosis performance. The application of this approach for an industrial machine is presented as an illustrative example.

 

Speaker’s Bio:

Clarence W. de Silva, P.Eng., Fellow ASME, Fellow IEEE, and Fellow Canadian Academy of Engineering, Fellow Royal Society of Canada
Professor of Mechanical Engineering 
Department of Mechanical Engineering
The University of British Columbia
http://ial.mech.ubc.ca/

 

Academic Qualifications:
Ph.D., Massachusetts Institute of Technology, USA, 1978
Ph.D.,University of Cambridge, England, 1998
D.Eng. (Honorary), University of Waterloo, Canada, 2008

 

Appointments:
Senior Canada Research Chair in Mechatronics and Industrial Automation,
Professorial Fellow
Peter Wall Scholar
NSERC-BC Packers Professor of Industrial Automation
Mobil Endowed Chair Professor
Honorary Chair Professor

 

Awards: Paynter Outstanding Investigator Award and Takahashi Education Award, ASME Dynamic Systems & Control Division; Killam Research Prize; Outstanding Engineering Educator Award, IEEE Canada; Lifetime Achievement Award, World Automation Congress; IEEE Third Millennium Medal; Meritorious Achievement Award, Association of Professional Engineers of BC; Outstanding Contribution Award, IEEE Systems, Man, and Cybernetics Society.

 

Fellowships: Lilly Fellow; NASA-ASEE Fellow; Senior Fulbright Fellow to Cambridge University; Fellow of the Advanced Systems Institute of BC; Killam Fellow; Erskine Fellow.



Editorial Boards: 14 journals including IEEE Trans. Control System Technology and ASME Journal of Dynamic Systems, Measurement & Control; Editor-in-Chief, International Journal of Control and Intelligent Systems; Editor-in-Chief, International Journal of Knowledge-Based Intelligent Engineering Systems; Senior Technical Editor, Measurements and Control; Regional Editor, North America, Engineering Applications of Artificial Intelligence—IFAC International Journal of Intelligent Real-Time Automation; andManagement Committee, IEEE-ASME Transaction in Mechatronics.

 

Publications: 23 technical books, 19 edited books, 51 book chapters, 250 journal articles, and 270 conference papers.

 

(2)  Generalization of Model Analysis to Decouple Linear Dynamic Systems

Keynote Speaker:
Professor Fai Ma, Fellow ASME
Department of Mechanical Engineering, University of California, Berkeley, USA
 

 

Abstract:

The purpose of this presentation is to report on an extension of modal analysis to reduce any linear dynamical system to independent single-degree-of-freedom subsystems in real space. To be specific, let ,  and  be arbitrary real square matrices of the same order with  assumed nonsingular. A real and invertible transformation is developed to convert  into  for which ,  are real and diagonal. As an important byproduct, a solution is thereby provided to the classical decoupling problem of damped linear systems. Design and analysis are often streamlined by system decoupling and several such applications will be mentioned.

 

Speaker’s Bio:

Dr. Fai Ma is Professor of Applied Mechanics in the Department of Mechanical Engineering, University of California at Berkeley. He received his B.S. degree from the University of Hong Kong in 1977 and his Ph.D. degree from the California Institute of Technology in 1981. From 1981 to 1986, he was a research engineer with Weidlinger Associates, the IBM Thomas J. Watson Research Center, and the Standard Oil Company. He is the recipient of several awards, which include a Presidential Young Investigator Award from the National Science Foundation, an Alexander von Humboldt Fellowship, and a Fulbright Senior Scholar Award. He is the author or co-author of more than 180 technical publications in the areas of vibration, system uncertainties, and stochastic simulation. He often serves as a consultant to industry and is a fellow of the American Society of Mechanical Engineers.

 

(3)  A Data-Centered World. On Blockchain, IoT and Big Data.

Keynote Speaker:
Professor Christian K. Hansen, Past President, IEEE Reliability Society
Department Chair and Professor of Statistics, Eastern Washington University, WA, USA

 

Abstract:

Through the last decade with the deployment of the Internet of Things (IoT) and Blockchain technology, Big Data has become the fabric and currency that bonds the world together.  While IoT has given rise to endless opportunities for sharing of data, Blockchain is the most well-known technology that enables securing the authenticity of data and transactions involving the exchange of data. This has given rise to the development of a number of new peer-to-peer systems, not limited to digital currency. In this presentation we review some historical trends and challenges related to this evolution of data and its related technologies and discuss areas of most likely focus over the next decade. In particular, ongoing efforts and future directions for research led by IEEE and the IEEE Reliability Society will be discussed.

 

Speaker’s Bio:

Dr. Christian K. Hansen served as President of the IEEE Reliability Society (2014-2016) and is currently Department Chair and Professor of Statistics in the Department of Mathematics, Eastern Washington University (EWU). He has been a faculty member at EWU since 1993 and served in a variety of academic and administrative leadership positions. He has been active in the statistics and reliability engineering profession for 30 years and published broadly on a variety of applications involving data derived from engineering systems. Over the past two decades, he has been active with the IEEE Reliability Society and has served in leadership positions that include vice-president of publications and treasurer before being elected to president in 2013. Dr. Hansen is a graduate of the Technical University of Denmark with degrees in Electrical Engineering (MS, 1988) and Statistics (PhD, 1991).