Experiments for PHM: needs, developments and challenges

Prof. Rui Kang
Chief Engineer and Chair Professor of School of Reliability and Systems Engineering
Beihang University, China

Prediction model is significant in the prognostics and health management (PHM), which is used to predict changes in the variables associated with the impending failure, and the establishment and verification of the model can be achieved through accelerated testing in the development process. Therefore, accelerated test method can be used in prognostics research as a way to assess the effects of the degradation process through time. It allows for the identification and study of different failure mechanisms and their relationships with different observable signals and parameters. This report discusses the applications and difficulties of the technology. The study presents two types of prediction models: one is the analytical model based on physical-driven, and the other is the empirical model based on data-driven. Afterwards, we enumerate the challenges of accelerated test in PHM application. First, it is essential that how to design accelerated tests to verify the accuracy and validity of the model, as whether the amount of information of the model is wide enough and the model can achieve the prediction of individual products in the normal use conditions. Second, the mechanism consistency border issue is also one of the prerequisites for the implementation of the accelerated test.

Rui Kang is Chief Engineer and Chair Professor of School of Reliability and Systems Engineering at Beihang University. He received his bachelor's and master’s degree in electrical engineering in 1987 and 1990 at Beihang University. He has developed six courses and has published eight books and more than 150 research papers. He is a famous expert in reliability of China industry. His main research interests include reliability technology for product design and experiment. He is the Chief Editor of the Journal of Reliability Engineering, and the founder of China prognostics and health management society. He received several types award from the Chinese government for his outstanding scientific research.

Recent Advances and Trends of Cloud-based PHM

Prof. Jay Lee
Ohio Eminent Scholar, L.W. Scott Alter Chair Professor, and Univ. Distinguished Professor
University of Cincinnati, USA
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Director, NSF Multi-Campus Industry/University Cooperative Research Center on Intelligent Maintenance Systems (IMS)
Univ. of Cincinnati, Univ. of Michigan, Missouri Univ. of S&T, Univ. of Texas-Austin
&
Preston Johnson, National Instruments

This keynote will introduce the most frequently used PHM tools for energy related asset management ad service innovation business design.
 As more smart software and embedded intelligence are integrated in industrial products and systems, prognostics technologies can further intertwine intelligent algorithms with electronics and embedded intelligence to predict product performance degradation and autonomously manage and optimize asset utilization and service needs,
The presentation will first introduce the current trends in smart product, manufacturing, and service transformation in global companies.  Second, recent advances and readiness of predictive technologies for next-generation engineering systems will be addressed. In addition, examples on wind turbine, green machines, smart battery for mobility and safety, will be used to illustrate the applications of smart analytics for self-maintenance and service systems.  Future direction in manufacturing to service transformation using dominant innovation will be introduced to illustrate how world-class companies and small to media size companies can transform to invisible market beyond competition.

Dr. Jay Lee is Ohio Eminent Scholar and L.W. Scott Alter Chair Professor at the Univ. of Cincinnati and is founding director of National Science Foundation (NSF) Industry/University Cooperative Research Center (I/UCRC) on Intelligent Maintenance Systems (IMS  www.imscenter.net ) which is a multi-campus NSF Industry/University Cooperative Research Center which consists of the Univ. of Cincinnati (lead institution), the Univ. of Michigan, and Missouri Univ. of S&T, and the Univ. of Texas-Austin.  Since its inception in 2001, the Center has been supported by over 75 global companies in 15 countries including P&G, GE Aviation, Toyota, Boeing, Caterpillar, Siemens, Chevron, Honeywell, Parker Hannifin, Spirit AeroSystems, Ingersoll Rand, Goodyear, Eaton, National Instruments Army Research Lab., Forcam (Germany), Alstom (France), Kistler (Switzerland), ITRI (Taiwan), Omron (Japan), Nissan (Japan), Samsung (Korea), Hitachi (Japan), Komatsu (Japan), Mitsubishi Heavy Industry (Japan), Toshiba (Japan), Delta Electronics (Taiwan), HIWIN (Taiwan), PMC (Taiwan), III (Taiwan), Shaanxi Auto Group (China), Tekniker (Spain), FMTC (Belgium), etc. His current research focuses on dominant innovation tools for product and service design as well as intelligent prognostics tools and smart predictive analytics for equipment reliability assessment and smart product life cycle management.
He serves as honorary professor and visiting professor for a number of institutions including Shanghai Jiao Tong Univ., City Univ. of Hong Kong, Cranfield Univ. in UK, Lulea Univ. of Technology in Sweden, Hong Kong PolyU., Xian Jiao Tong Univ. and Harbin Institute of Technology (HIT) in China.  He also serves as advisor to a number of global organizations, including a member of the Manufacturing Executive Leadership Board of U.S., IBM MAXIMO Executive Advisory Council, Industrial Technology Research Institute (ITRI) in Taiwan, Japan Productivity Center (JPC), Academy of Machinery Science & Technology in China, Scientific Advisory Board of Flanders' MECHATRONICS Technology Centre (FMTC) in Leuven, Belgium, etc. In addition, he serves as editors and associate editor for a number of journals including IEEE Transaction on Industrial Informatics, Int. Journal on Prognostics & Health Management (IJPHM), Int. Journal on Service Operations and Informatics, etc,
Previously, he served as Director for Product Development and Manufacturing at United Technologies Research Center (UTRC), E. Hartford, CT as well as Program Directors for a number of programs at NSF during 1991-1998, including the Engineering Research Centers (ERCs) Program, the Industry/University Cooperative Research Centers (I/UCRCs) Program, and the Div. of Design, Manufacture, and Industrial Innovation. He also served as an advisory member for a number of institutions including, Johns Hopkins Univ., Cambridge Univ., etc.
He has authored/co-authored numerous highly influential articles and technical papers in the areas of machinery monitory and prognostics, E-manufacturing, and intelligent maintenance systems.  He also has a number of patents and trademarks.  He is a frequently invited speaker and has delivered over 160 invited keynote and plenary speeches at major international conferences.  He is a Fellow of ASME, SME, as well as a founding fellow of International Society of Engineering Asset Management (ISEAM).  He and his Team won PHM Data Challenge Competition in 2008, 2009, 2011 and also received National Instruments Prognostics Innovation Award in 2012.

Particle filtering for PHM: recent advancements and future challenges

Dr. Marcos Orchard
Associate Professor, Department of Electrical Engineering, Universidad de Chile

Particle filters (PF) have been established as the de facto state-of-the-art in failure prognosis, and particularly in the representation of the uncertainty that is associated to long-term predictions. This talk explores some of the most important aspects associated to the problem of failure prognosis, with special emphasis on the utilization of Particle Filter (PF) algorithms and outer feedback correction loops for uncertainty representation and management. Application examples will include problems associated to battery management systems, mining industry, and volatility prediction in finance.

Dr. Marcos E. Orchard is Associate Professor with the Department of Electrical Engineering at Universidad de Chile, Associate Researcher at the Advanced Mining Technology Center, and Project Leader at the Lithium Innovation Center. His current research interest is the design, implementation and testing of real-time frameworks for fault diagnosis and failure prognosis, with applications to battery management systems, mining industry, and finance. For nearly ten years, Dr. Orchard has been exposed to the theoretical aspects of nonlinear state estimation and worked extensively in research projects regarding system identification, fault detection and failure prognosis. His fields of expertise include statistical process monitoring, parametric/non-parametric modeling, and system identification. His research work at the Georgia Institute of Technology was the foundation of novel real-time fault diagnosis and failure prognosis approaches based on particle filtering algorithms. He received his Ph.D. and M.S. degrees from The Georgia Institute of Technology, Atlanta, GA, in 2005 and 2007, respectively. He received his B.S. degree (1999) and a Civil Industrial Engineering degree with Electrical Major (2001) from Catholic University of Chile. Dr. Orchard has published more than 50 papers in his areas of expertise.

Using PHM to Keep the Dreamliner in the Clouds

Prof Michael Pecht
Chair Professor in Mechanical Engineering; Professor in Applied Mathematics at the University of Maryland, USA

Over the past year, Boeing’s 787 Dreamliners have experienced a number of technical issues including electrical arcing in the aircraft’s main power panel and catastrophic failure of the batteries used to run the aircraft’s auxiliary power unit.  These and many other in-flight failures in the 787, suggest that the traditional reliability approaches of product design, test, and risk assessment that Boeing and their suppliers implemented are flawed.  Considering the severity of the failures experienced, Boeing is fortunate that serious injuries or death did not occur as a result.  It remains to be seen how the failures will affect Boeing’s public image in the long run, and whether customers will be lining up for Dreamliner flights once the airplanes are cleared for flight again.  One more failure could devastate Boeing and indefinitely ground the Dreamliner.
In this presentation, the use of prognostics and systems health management is discussed for battery management to forecast battery life and safety.  Batteries represent complex electrochemical structures with conflicting failure mechanisms. The prognostics and systems health management approach incorporates both physics-of-failure techniques to understand the mechanisms that precede failure and data-driven concepts to provide a prognosis before the actual failure event occurs.  It serves as a model for how companies should not only conduct reliability assessments and qualification, but also real-time safety and health monitoring. 

Prof Michael Pecht is a world renowned expert in strategic planning, design, test, IP and risk assessment of electronic products and systems. In 2011, he received the University of Maryland’s Innovation Award for his new concepts in risk management. In 2010 he received the IEEE Exceptional Technical Achievement Award for his innovations in the area of prognostics and systems health management.  In 2008, he was awarded the highest reliability honor, the IEEE Reliability Society’s Lifetime Achievement Award. Prof Pecht has an MS in Electrical Engineering and an MS and PhD in Engineering Mechanics from the University of Wisconsin at Madison. He is a Professional Engineer, an IEEE Fellow, an ASME Fellow, an SAE Fellow and an IMAPS Fellow. He has previously received the European Micro and Nano-Reliability Award for outstanding contributions to reliability research, 3M Research Award for electronics packaging, and the IMAPS William D. Ashman Memorial Achievement Award for his contributions in electronics analysis. He is the editor-in-chief of IEEE Access, and served as chief editor of the IEEE Transactions on Reliability for nine years, chief editor for Microelectronics Reliability for sixteen years, an associate editor for the IEEE Transactions on Components and Packaging Technology, and on the advisory board of IEEE Spectrum. He is the founder and Director of CALCE (Center for Advanced Life Cycle Engineering) at the University of Maryland, which is funded by over 150 of the world’s leading electronics companies at more than US$6M/year. The CALCE Center received the NSF Innovation Award in 2009. He is currently a Chair Professor in Mechanical Engineering and a Professor in Applied Mathematics at the University of Maryland. He has written more than twenty books on product reliability, development, use and supply chain management and over 600 technical articles.  He has also written a series of books of the electronics industry in China, Korea, Japan and India. He consults for 22 international companies.