"Systems Engineering Approach to PHM in Aerospace" by Mr. Kurt James Doughty
"Condition Monitoring & Diagnostics of Nuclear Mechanical Components" by Dr. Jin-Ho Park
"Prognostics and Health Management for Rechargeable Batteries" by Prof. Kwok L. Tsui
"Trends and Recent Advances of Industrial Big Data Analytics and Cyber Physical Systems for PHM Applications" by Prof. Jay Lee
"PHM, a Continuum: the Past, Present, and Future" by President Andrew Hess
“Systems Engineering Approach to PHM in Aerospace”
Kurt James Doughty
UTC Aerospace Systems
Bio: Kurt Doughty is Senior Engineer of PHM and Data Analytics at UTC Aerospace Systems (UTAS). He has led the development of an Aerospace PHM Center of Excellence in Singapore since February 2015 by creating and validating prognostic analytic models and driving proactive maintenance to avoid aircraft schedule interruptions.
Kurt has developed standards for advanced reliability analyses to quantify component failure risk and to support component scheduled replacement plans. He has established processes for engineering teams to manage in-service data review, maintenance recommendations, communication with airlines, and airline maintenance activity. He also provides training for aircraft systems, reliability analyses, prognostics, and data analytics.
Kurt has worked for UTC Aerospace Systems (formerly Hamilton Sundstrand) since 2001. Prior to his PHM and reliability work in Singapore, Kurt supported aircraft final assembly and delivery (FA&D) functional and flight test for UTAS systems and components on the Boeing 787. He held senior system engineering positions leading design and verification of aerospace components, system controls, and integrated system labs.
Kurt has been awarded five US patents related to aircraft system controls and design. He received his B.S. in Engineering Physics and M.S. in Mechanical Engineering from the University of Connecticut.
“Condition Monitoring & Diagnostics of Nuclear Mechanical Components”
Dr. Jin-Ho Park Korea Atomic Energy Research Institute Korea
Bio: Jin-Ho Park is a principal researcher at Korea Atomic Energy Research Institute. He has done research in vibration & acoustic analysis, R&D on structural safety analysis of mechanical components in nuclear power plants, technology development for mechanical system's integrity monitoring/diagnostics/prognostics, development of vibration reduction techniques for the plant piping systems, development of nuclear steam supply system integrity monitoring & diagnosis technologies such as reactor internals vibration monitoring, loose part monitoring, acoustic leak monitoring, and reactor coolant pump vibration monitoring, R&D on condition based maintenance technology, etc.
He is a member of the Korea Society for Noise and Vibration Engineering and the Korean Nuclear Society. Park has a B.S. and M.S. in Mechanical Engineering from Pusan National University, Busan, South Korea. He has a Ph.D. in Mechanical Engineering from Korea Advanced Institute of Science & Technology, Daejeon, South Korea.
“Prognostics and Health Management for Rechargeable Batteries”
Prof. Kwok L. Tsui
City University of Hong Kong Hong Kong
Bio: Kwok L Tsui is head and chair professor in the Department of Systems Engineering and Engineering Management at City University of Hong Kong. Prior to the current position, Dr. Tsui has been professor/associate professor in the School of Industrial and Systems Engineering at Georgia Institute of Technology in 1990-2011; and member of technical staff in the Quality Assurance Center at AT&T Bell Labs in 1986-1990. He received his Ph.D. in Statistics from the University of Wisconsin at Madison. Professor Tsui was a recipient of the National Science Foundation Young Investigator Award. He is Fellow of the American Statistical Association, American Society for Quality, International Society of Engineering Asset Management, and Hong Kong Institution of Engineers; elected council member of the International Statistical Institute; and U.S. representative to the ISO Technical Committee on Statistical Methods. Professor Tsui was Chair of the INFORMS Section on Quality, Statistics, and Reliability and the Founding Chair of the INFORMS Section on Data Mining. Professor Tsui’s current research interests include big data analytics, surveillance in healthcare and public health, prognostics and systems health management, calibration and validation of computer models, process control and monitoring, and robust design and Taguchi methods.
"Trends and Recent Advances of Industrial Big Data Analytics and Cyber Physical Systems for PHM Applications"
Prof. Jay Lee
University of Cincinnati
Bio: Dr. Jay Lee is Ohio Eminent Scholar, L.W. Scott Alter Chair Professor, and Distinguished Univ. 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 (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, Missouri Univ. of S&T, and the Univ. of Texas-Austin. Since its inception in 2001, the Center has been supported by over 85 global companies including P&G, GE Aviation, Eaton, National Instruments, Boeing, Goodyear, Toyota, Caterpillar, Siemens, Chevron, Honeywell, Parker Hannifin, Spirit AeroSystems, Ingersoll Rand, Intel, Applied Materials, Automated Precision Inc (API), Bosch Rexroth (Germany), Alstom (France), Omron (Japan), Nissan (Japan), Tekniker (Spain), FMTC (Belgium), Kistler (Switzerland), Samsung (Korea), Shanghai Electric (China), Baosteel (China), etc. He is one of the pioneers in the field of Prognostics and Health Management (PHM) and has mentored his students and won 1st prize of PHM Data Challenges five times since 2008. He also mentored his students and developed a spin-off company Predictronics through NSF ICorps Award in 2012.
He serves as Committee member for White House Cyber Physical Systems (CPS) American Challenge Program in Dec. 3013, a member of Technical Executive committee (TEC) of Digital Manufacturing and Design Innovation (DMDI) in Feb. 2014, as well as a member of Leadership Council of MForesight which is a NSF/NIST Newly established manufacturing think tank in Sept. 2015. He also serves as honorary professor and visiting professor for a number of institutions including Shanghai Jiao Tong Univ., Cranfield Univ. in UK, Lulea Univ. of Technology in Sweden, etc. He also serves as advisor to a number of global organizations, including a member of the Manufacturing Executive Leadership Board of U.S., Scientific Advisory Board of Flanders' MECHATRONICS Technology Centre (FMTC) in Leuven, Belgium, International S&T Committee for Alstom, France, Scientific Committee of SIMTech of Singapore, 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 Prognostics and Health Management, E-Manufacturing, Industry 4.0, and Cyber Physical Systems in Manufacturing, etc. He has over 20 patents and trademarks. He is a frequently invited speaker and has delivered over 300 invited speeches worldwide including over 200 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.
He has received a number of awards including the most recent Prognostics Innovation Award at NI Week by National Instruments in 2012 and NSF Alex Schwarzkopf Technological Innovation Prize and MFPT (Machinery Failure Prevention Technology Society) Jack Frarey Award in 2014. In 1994, he received President Clinton’s Appreciation Letter for his participation and contribution to the United States Partnership for Next Generation Vehicle (PNGV) Program. He is also a honorary advisor to the Heifer International-a charity organization working to end hunger and poverty around the world by providing livestock and training to struggling communities.
"PHM, a Continuum: the Past, Present, and Future"
President of PHM Society USA
Bio: Andy Hess is globally recognized as a leader and expert in the fields of advanced diagnostics, Prognostics and Health Management (PHM), Condition Based Maintenance (CBM+), asset management, big data analytics, and predictive maintenance. For over 35 years, at the Naval Air System Command, Andy lead the innovation, development, and implementation of condition monitoring systems for all the Navy fixed wing and helicopter applications. He is widely recognized as a leader in the area of turbine engine monitoring systems. Andy helped formulate the autonomic logistics concept and lead the PHM development for the JSF F-35 program. Andy is a widely used consultant to industry, government, and academic organizations in the fields of advanced diagnostics, prognostics, health and asset management, and enterprise wide applications. Andy is the current president of the PHM Society and remains active in many other professional, advisory, and standards organizations and committees. Andy started his career in flight testing at the Naval Air Test Center and Naval Air Warfare Center evaluating aircraft systems; developing the first comprehensive engine monitoring system; and playing significant roles in the development of military aircraft. He has been a Senior Engineering Fellow at NAVAIR and a Fellow of the Society for Integrated Engineering Asset Management. He lead the PHM effort for the Joint Strike Fighter JPO. Through his consulting firm, Andy helped DARPA structure and manage their large materials and structures based Prognosis program. Some of his other clients have included: Bell Helicopter, Boeing, General Atomics, NASA Ames, Honeywell, the US Army CECOM, Sikorsky, Teledyne Controls, the SAS Institute, the Australian and Canadian governments, the University of Maryland CALCE, and sundry small businesses.
Prof. Jay Lee
University of Cincinnati,
Trends and Recent Advances of
Industrial Big Data Analytics and Cyber Physical Systems
for PHM Applications
Ohio Eminent Scholar, L.W. Scott Alter Chair, and Distinguished Univ. Professor
Univ. of Cincinnati Jay.firstname.lastname@example.org
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-
In today’s competitive business environment, companies are facing
challenges in dealing with big data issues for rapid decision making for improved
productivity and business innovation. Many product and manufacturing systems
are not ready to manage big data due to the lack of smart analytics tools. U.S.
has been driving the Cyber Physical Systems (CPS), Industrial Internet, and
Advanced Manufacturing Partnership (AMP) Program to advance future
manufacturing. Germany is leading a transformation toward 4th Generation
Industrial Revolution (Industry 4.0) based on Cyber-Physical Production System
(CPPS). It is clear that as more predictive analytics software and embedded IoT
are integrated in industrial products, predictive technologies can further intertwine
smart IoT to predict product performance autonomously and further optimize the
smart service systems.
The presentation will address the trends of predictive big data analytics
and CPS for future industrial PHM application. First, predictive analytics and
Cyber-Physical System (CPS) enabled industrial systems will be introduced.
Second, advanced predictive analytics technologies for self-aware industrial
systems with case studies will be presented. Finally, business innovation based
on industrial big data will be introduced using case studies.
President of PHM Society, USA
PHM, a Continuum: the Past, Present, and Future
This Keynote Presentation will explore PHM from the perspective of being a continuum of ever evolving and increasingly effective capabilities. Background experience, particularly in aerospace applications, will be reviewed going from basic diagnostic capabilities to the evolution of the Prognostics and Health Management (PHM) concept. The present state-of-the art of advanced diagnostic and prognostic capabilities; including physics of failure, actual life remaining and prediction models will be explored. Current predictive analytic capabilities will be discussed as they are being applied to platform and component asset management and enterprise-wide solutions. The spread of the PHM concept to many new industry sectors and future applications will be identified. Some significant lessons learned during the evolution of PHM will be cited. The trajectory of future PHM capabilities and applications will be discussed including their use in smart manufacturing and human performance.
Kurt James Doughty
UTC Aerospace Systems, Singapore
Systems Engineering Approach to PHM in Aerospace
Systems engineering approaches to PHM are established by first capturing the right data. Understanding prominent component failure modes and their system level effects allows engineers to target necessary sensing feedback to enable precise prognostics algorithm development. For many commercial aircraft, however, requirements for PHM are either not identified or not prioritized in advance of entry into service (EIS). As the design of aircraft systems evolve, adding sensors is essential to comply with baseline performance requirements, such as robust operation throughout the design envelope, fault detection via built-in test (BIT), and safety via backup and protective controls. Conversely, weight reduction efforts during aircraft systems design often result in lean sensor architectures. Incorporating PHM requirements early in the aircraft design process will enable more robust sensor architectures and enhance PHM capabilities.
This presentation will review design considerations for commercial aircraft systems as they relate to PHM; including sensor provisioning and BIT functionality. Examples of prognostics algorithm development for legacy system architectures will be discussed. The future of designing aircraft systems for PHM will also be explored.
Prof. Kwok L. Tsui
City University of Hong Kong, Hong Kong
Prognostics and Health Management for Rechargeable Batteries
After numerous successful applications of statistical quality control and reliability methods in manufacturing industries in the past several decades, the quality paradigm has shifted from manufacturing application to field operation in many industries. This shift was triggered by continuous concerns in product reliability, system safety, and failure prevention, and made possible by latest advancement in data collection technologies and development of powerful modelling algorithms. This general field of research provides tremendous opportunities for interdisciplinary research and has been classified as prognostics and system health management (PHM). In this talk we will share our experience of PHM research in the application of rechargeable batteries through methods in data mining, diagnostics and prognostics, anomaly detection and monitoring.
Dr. Jin-Ho Park
Korea Atomic Energy Research Institute, Korea
Condition Monitoring & Diagnostics of Nuclear Mechanical Components
Typically, there exist two pressure boundary systems in a PWR(Pressurized Water Reactor) NPP(Nuclear Power Plant). One is the reactor coolant system(RCS) called as the primary system or Nuclear Steam Supply System(NSSS). The other is balance of power(BOP) called as the secondary system. The key mechanical components of the primary system is mainly comprised of reactor pressure vessel(RPV), pressurizer(PZR), steam generator(SG), reactor coolant pump(RCP) and pipings. The NIMS(NSSS Integrity Monitoring System) has been developed to monitor and diagnose the structural safety of the NSSS components on an on-line basis. In this paper, the Korean NIMS developed by KAERI is introduced. It is comprised of four independent sub-systems such as IVMS(Internal Vibration Monitoring System), LPMS(Loose Part Monitoring System), ALMS(Acoustic Leakage Monitoring System), and RCPVMS(Reactor Coolant Pump Vibration Monitoring System). The IVMS has been developed for early detection of the degradation of the preload condition of the reactor internal components by measuring the change of vibratory modal frequencies of the core barrel assembly. The LPMS is to monitor the presence of a loosened or detached metallic object within the reactor coolant system using the vibration sensors installed on the surface of the system. The primary purpose of the ALMS is to monitor coolant leakage at the potential leak regions such as the reactor vessel, welded region in pipings, and valves, etc. The second purpose is to detect initiation of crack on the surface of the pressure boundary of the reactor coolant system. The RCPVMS is to provide diagnostic information for detecting such symptoms as the shaft crack, the RCP misalignment, rotor unbalance, and the abnormalities of support bearings.