"What are the effects of the reliability model uncertainties in the maintenance decisions?" Prof. Bruno Castanier, Angers University, France
"New prognostics-based structural maintenance strategies for civil aircraft" Prof. Christian Gogu, University in Toulouse III, France
"Novel PHM concept for future use in safety relevant electronics for harsh environment" Dr. Przemyslaw Gromala, Robert Bosch GmbH, Germany
"Maintenance Analytics, industrial data science and virtual commissioning" Prof. Diego Galar, Lulea University of Technology, Sweden
"Health Monitoring and Vibratory Fault Prediction of Rotating Machinery" Prof. Qingkai Han, Dalian University of Technology, China
"Condition Monitoring of Automotive Smart Systems Utilizing Piezoresistive Stress Sensor" Prof. Bongtae Han, University of Maryland College Park, USA
"Ensemble Learning with Degradation-Dependent Weights for Remaining Useful Life Prediction" Prof. Chao Hu, Iowa State University, USA
"PHM Frontiers: Physics-based Approach to Deep Learning" Prof. Byeng D. Youn, Seoul National University, Korea
"Deep Learning Based Virtual Metrology (VM) and Yield Prediction in Semiconductor Manufacturing Processes" Prof. Myong-Kee Jeong, Rutgers University, USA
"Physics-based prognostics-promises and challenges" Prof. Namho Kim, University of Florida, USA
"Metamaterial-based Enhancement of Elastic Wave Energy Harvesting" Dr. Miso Kim, Korea Research Institute of Standards and Science, Korea
"Recent Advances In In-process NDE and Smart Hangar for Aerosapce SHM" Prof. Jung Ryul Lee, KAIST, Korea
"Selective Maintenance Strategies for Complex Systems Under Uncertainty" Prof. Yu Liu, University of Electronic Science and Technology of China, China
"Probabilistic Fatigue Life Prognosis for Steel Railway Bridges after Local Inspection and Repair" Prof. Young Joo Lee, Ulsan National Institute of Science and Technology, Korea
"Data-Driven Based Battery Health Prognosis with Diagnosis Uncertainties and Insufficient Training Data Sets" Prof. Zhimin Xi, University of Tennessee Knoxville, USA
"Model- and Non-model-based Damage Detection Methods Using Vibration Data" Prof. Weidong Zhu, University of Maryland Baltimore Country, USA
“What are the effects of the reliability model uncertainties in the maintenance decisions?”
Prof. Bruno Castanier University of Angers France
Bio: Since he got his PhD in 2001, Bruno CASTANIER develops his research in Maintenance Optimization mainly focused on the construction of stochastic models for the condition-based maintenance of gradually deteriorating systems. Ha has published close to 20 papers in journals, such as Reliability Engineering and Systems Safety and Journal of Risk and Reliability, and more than 40 international conference proceedings. Since 2014, Bruno CASTANIER is professor at ISTIA Engineering school at Angers and the head of the SFD (Reliability Engineering and Decision-Making tools) research team of the LARIS lab of the University of Angers.
“New prognostics-based structural maintenance strategies for civil aircraft”
Prof. Christian Gogu Universite Toulouse III France
Bio: Christian Gogu is Associate Professor in the department of Mechanical Engineering at Universite Toulouse III (France). He received his Master degree in Mechanical Engineering from the Ecole des Mines de Saint Etienne (France) in 2006 and his PhD in 2009 as part of a joint PhD program between the Ecole des Mines de Saint Etienne and the University of Florida. He has been granted an award for outstanding academic achievement as part of his PhD on Bayesian identification of orthotropic elastic constants identification. His research interests include design under uncertainty, multidisciplinary design optimization, structural health monitoring and surrogate modeling with applications mainly to aerospace structural design.
“Novel PHM concept for future use in safety relevant electronics for harsh environment”
Dr. Przemyslaw Gromala Robert Bosch GmbH, Automotive Electronics Germany
Bio: Mr Przemyslaw Gromala is a simulation senior expert at Robert Bosch GmbH, Automotive Electronics in Reutlingen. Currently leading an international simulation team and FEM verification lab with the main focus on implementation of simulation driven design for electronic control modules and multi-chip power packaging for hybrid drives. His research activities focus on virtual pre-qualification techniques for development of the electronic control modules and multi-chip power packaging. His technical expertise includes material characterization and modeling, multi-domain and multi-scale simulation incl. fracture mechanics, verification techniques and prognostics and health monitoring for future safety related electronic smart systems.
Prior joining Bosch Mr Gromala worked at Delphi development center in Krakow, as well as at Infineon research and development center in Dresden.
He is an active committee member of the IEEE conferences: ECTC, EuroSimE, ICEPT; ASME: InterPACK. Active committee member of EPoSS - defining R&D and innovation needs as well as policy requirements related to Smart Systems Integration and integrated Micro- and Nanosystems.
He holds a PhD in mechanical engineering from Cracow University of Technology in Poland.
“Maintenance Analytics, industrial data science and virtual commissioning”
Prof. Diego Galar Lulea University of Technology Sweden
Bio: Dr. Diego Galar is Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering at LTU, Luleå University of Technology where he is coordinating several H2020 projects related to different aspects of cyber physical systems, Industry 4.0, IoT or industrial Big Data. He was also involved in the SKF UTC centre located in Lulea focused on SMART bearings. He is also actively involved in national projects with the Swedish industry and also funded by Swedish national agencies like Vinnova.
He is also principal researcher in Tecnalia (Spain), heading the Maintenance and Reliability research group.
He has authored more than three hundred journal and conference papers, books and technical reports in the field of maintenance, working also as member of editorial boards, scientific committees and chairing international journals and conferences.
In industry, he has been technological director and CBM manager of international companies, and actively participated in national and international committees for standardization and R&D in the topics of reliability and maintenance.
In the international arena, he has been visiting Professor in the Polytechnic of Braganza (Portugal), University of Valencia and NIU (USA). Currently, he is visiting professor in University of Sunderland (UK) and University of Maryland (USA), also guest professor in the Pontificia Universidad Católica de Chile
“Health Monitoring and Vibratory Fault Prediction of Rotating Machinery”
Prof. Qingkai Han Dalian University of Technology
Bio: He obtained B.S. and M.S. degrees in Mechanical Engineering from Liaoning University of Technology (china) in 1990 and 1993, and obtained Ph.D. degree in Mechanical Engineering from Northeastern University (China) in 1997. He is the duty professor of discipline of Mechanical Design and Theory in School of Mechanical Engineering, Dalian University of Technology (China). He was ever the associate professor and professor of Mechanical Engineering in Northeastern University (China) before 2012.
His research areas of interest are: Mechanical dynamics and vibration control, condition monitoring and fault diagnosis, data processing and software.
He now is responsible for some research projects including The National Programs on Key Basic Research (973 Programs) and The Natural Science Funds of China (NSFCs).
He is the member of IFToMM and the vice chairman of Chinese region, the member of council of Chinese Society for Vibration Engineering, and the member of Chinese Society of Theoretical and Applied Mechanics, etc.
He has published over 200 journal papers including 40 papers indexed by SCI and 102 papers indexed by EI, and 11 books. He owns 20 patients and software-copyrights. He was invited as plenary speakers by IUTAM Rotor Dynamics Symposium 2009, Moss 2013, CEDC 2013, and gave seminars in many universities and institutes. He was the conference chairmen of CEDC (2012, 2013, 2014, 2015), and ICMIT (2011, 2015) and Int-Vib-Impact Conference 2011 and so on.
He has gained 8 science-technology awards of national or local government totally. He was ever listed in the Program of New Century Excellent Talents in University by Ministry of Education, and rewarded by Henry Fok University Young Teacher Award and Liaoning Province's Excellent Young Researchers.
“Condition Monitoring of Automotive Smart Systems Utilizing Piezoresistive Stress Sensor”
Prof. Bongtae Han Mechanical Engineering Department
University of Maryland
Bio: Dr. Bongtae Han received his BS and MS degrees from Seoul National University in 1981 and 1983, respectively, and his Ph.D. degree in Engineering Mechanics from Virginia Tech in 1991. Dr. Bongtae Han is Keystone Professor of Engineering and APT Chair of the Mechanical Engineering Department of the University of Maryland; and is currently directing the LOMSS (Laboratory for Optomechanics and Micro/nano Semiconductor/Photonics Systems) of CALCE (Center for Advanced Life Cycle Engineering).
Dr. Han has co-authored a text book entitled "High Sensitivity Moire: Experimental Analysis for Mechanics and Materials", Springer-Verlag (1997) and edited two books. He has published 12 book chapters and over 250 journal and conference papers in the field of microelectronics, photonics and experimental mechanics. He holds 2 US patents and 4 invention disclosures.
Dr. Han received the IBM Excellence Award for Outstanding Technical Achievements in 1994. He was a recipient of the 2002 Society for Experimental Mechanics (SEM) Brewer Award for his contributions to development of photomechanics tools used in semiconductor packaging. Most recently, he was named the 2016 American Society of Mechanical Engineering (ASME) Mechanics Award winner in Electronic and Photonic Packaging Division for his contributions to structural mechanics of electronic systems. His publication awards include (1) the Year 2004 Best Paper Award of the IEEE Transactions on Components and Packaging Technologies, (2) the Gold Award (best paper in the Analysis and Simulation session) of the 1st Samsung Technical Conference in 2004 and (3) the Year 2015 Best Paper Award of the 16th International Conference on Electronic Packaging Technology (ICEPT 2015). His contributions to an innovative 1,500-face lumen LED luminaire, jointly developed with GE, have been recognized in a Press Release (Oct. 21, 2010, MarketWatch.com, The Wall Street Journal). He served as an Associate Technical Editor for Experimental Mechanics, from 1999 to 2001, and also served as an Associate Technical Editor for Journal of Electronic Packaging, Transaction of the ASME from 2003 to 2012.
He was elected a Fellow of the SEM and the ASME in 2006 and 2007, respectively.
“Ensemble Learning with Degradation-Dependent Weights for Remaining Useful Life Prediction”
Prof. Chao Hu Iowa State University USA
Bio: Dr. Chao Hu received his B.E. degree in Engineering Physics from Tsinghua University in Beijing, China in 2007 and the Ph.D. degree in Mechanical Engineering at the University of Maryland, College Park in Maryland in 2011. He worked as a Principal Scientist at Medtronic, Inc. in Minnesota from 2011 to 2015. He is currently an Assistant Professor in the Department of Mechanical Engineering at the Iowa State University. His research interests are engineering design under uncertainty, design of lithium-ion energy storage systems, and prognostics and health management (PHM). Dr. Hu has received several awards and recognitions for his research, including: the Highly Cited Research Paper 2012-2013 in the Journal of Applied Energy in 2015; the Star of Excellence Individual Award at Medtronic in 2014; the Best Paper Award in the ASME Design Automation Conferences (DAC) in 2013; a nomination for the 2013 Eni Award Renewable Energy Prize, known as "Nobel Prize in Energy Sector"; the Best Paper Award in the IEEE PHM Conference in 2012; and two-time receipts of the Top 10 Best Paper Award in the ASME DACs in 2011 and 2012. His research work has led to more than 50 peer-reviewed publications in the above areas.
“PHM Frontiers: Physics-based Approach to Deep Learning”
Prof. Byeng D. Youn Seoul National University Korea
Bio: Prof. Byeng D. Youn is the Professor of Mechanical and Aerospace Engineering at Seoul National University (SNU) and the CEO of OnePredict Inc. (onepredict.com). Before joining SNU, he used to be the Assistant Professor in the Department of Mechanical Engineering at the University of Maryland, College Park. He is currently the Future-Tech Advisory Board Member of LG Electronics. He earned Ph.D. degree from the University of Iowa in 2001. His dedication and efforts in research have garnered substantive peer recognition resulting in many notable awards including the ISSMO/Springer Prize for a Young Scientist (2005), the IEEE PHM Competition Winner (2014), the PHM Society Data Challenge Winners (2014, 2015), the ASME IDETC Best Paper Awards (2001, 2008), etc. He has over 250 peer-reviewed publications and 4,300 citations in the area of PHM, reliability analysis and design, and energy harvesting. He also serves as an Associate Editor / Editorial Board of many notable journals including Structural and Multidisciplinary Optimization (SMO), International Journal of Precision Engineering and Manufacturing - Green Technology (IJPEM-GT), and so on.
“Deep Learning Based Virtual Metrology (VM) and Yield Prediction in Semiconductor Manufacturing Processes”
Prof. Myong-Kee Jeong Rutgers University USA
Bio: Myong K. (MK) Jeong is the Professor in the Department of Industrial and Systems Engineering and RUTCOR (Rutgers Center for Operation Research), Rutgers, the State University of New Jersey, USA. He received a B.S. in Industrial Engineering from Han Yang University, Seoul, Korea, in 1991, an M.S. in Industrial Engineering from KAIST (Korea Advanced Institute of Science and Technology), Taejon, Korea, in 1993, an M.S. in Statistics and a Ph.D. in Industrial and Systems Engineering from the Georgia Institute of Technology, Atlanta, Georgia, in 2002 and 2004, respectively. He worked as a senior researcher from 1993 to 1999 at the Electronics and Telecommunications Research Institute (ETRI). His research interests include sensor data analytics, process prognostics and diagnostics, machine learning, data mining, quality and reliability engineering, and pattern recognition. He received the prestigious Freund International Scholarship and the National Science Foundation (NSF) CAREER Award in 2002 and 2007, respectively. His research has been funded by the NSF, United States Department of Agriculture (USDA), National Transportation Research Center, Inc. (NTRCI), and industries. He has been a consultant for Samsung Electronics, Intel, ETRI, and other companies. He has published more than 90 refereed journal papers. He served as the President of the INFORMS Data Mining Section. He has been serving as an Associate Editor of the IEEE Transaction on Automation Science and Engineering, Advisory Board Member of International Journal of Advanced Manufacturing Technology, Associate Editor, and International Journal of Quality, Statistics and Reliability.
“Physics-based prognostics-promises and challenges”
Prof. Nam-Ho Kim
University of Florida USA
Bio: Dr. Nam-Ho Kim is presently Professor and Daniel C. Drucker Faculty Fellow of Mechanical and Aerospace Engineering at the University of Florida. He graduated with a Ph.D. in the Department of Mechanical Engineering from the University of Iowa in 1999 and joined the University of Florida in 2002. His research area is structural design optimization, design under uncertainty, structural health monitoring, and nonlinear structural mechanics. He has published six books and more than hundred fifty refereed journal and conference papers in the above areas. In particular, he is the leading author of Prognostics and Health Management of Engineering Systems: An Introduction (2016)
“Metamaterial-based Enhancement of Elastic Wave Energy Harvesting”
Dr. Miso Kim
Korea Research Institute of Standards and Science Korea
Bio: Miso Kim is a senior research scientist at Korea Research Institute of Standards and Science (KRISS). She received her undergraduate degree in Materials Science and Engineering from Seoul National University, South Korea (2004). She received her M.S. (2007) and Ph.D. degrees (2012) in Materials Science and Engineering from the Massachusetts Institute of Technology (MIT). She joined the Center for Safety Measurement of KRISS as a senior research scientist in 2012 and has happily pursued her passion for research at KRISS since then. Her primary research interests cover a wide range of analytical modeling and experimental verification for piezoelectric materials and their applications, particularly mechanical energy harvesting. She's been an active committee member of Energy Harvesting Workshop (EHW) and International Workshop on Piezoelectric Materials and Applications (IWPMA).
“Recent Advances in In-process NDE and Smart Hangar for Aerosapce SHM”
Prof. Jung Ryul Lee
Bio: J R Lee (Full name in Korean: Jung-Ryul Lee) is associate professor of the Department of Aerospace Engineering in Korea Advanced Institute of Science and Technology, South Korea. He served as a co-director of the Engineering Institute-Korea between Los Alamos National Lab and Chonbuk National University from July 2011 to Dec 2014. He was a visiting scholar of Los Alamos National Laboratory in US from Aug 2013 to July 2014.
He received his MS from KAIST in Korea, and Ph.D from Ecole Nationale Superieure Des Mines de Saint-Etienne in France with the 1st class honor in 2004. Before joining the university, he has been a scientific staff member at the National Institute of Advanced Industrial Science and Technology in Japan and a research associate at Ecole Nationale Superieure Des Mines De Saint-Etienne. His research interest includes Smart Hangar (Inventor), integrated systems health monitoring, fiber optic, remote and wireless sensing, advanced nondestructive evaluation and measurement, pyroshock, laser ultrasonics, optics in engineering, microwave imaging, and diagnostic artificial intelligence. His application field encompasses space launchers, unmanned air vehicles, engines, wind turbines, power plants, railway structures, automobiles, public safety and radome/stealth structures. He has published over 300 articles and patents in this area, and received several awards, including Emerging Researcher Award in 2007 by the Japanese Society for Nondestructive Inspection, one of 16 Excellent Emerging Researchers Award in 2011 by Ministry of Education, Science & Technology in Korea, Excellent R&D Achievement Award in 2013 by Jeonbuk Province, and Young Scientist Awards in 2015 and 2016 by Korean Composite Material Society and International Sustainable Aviation Research Society (SARES), 2016 KAIST R&D 10, respectively. His research team was appointed as Global Research & Development Center in 2011 and Boeing-KAIST Technical Contact Lab. A proof-of-concept paper of Smart Hangar authored by him and his student won the grand prize of the best papers in 2013 from Ministry of Land, Infrastructure and Transport in Korea.
He serves a co-chair of International Conference on ASHMCS2012 and 2014, and an editorial board member of SHM-IJ, ACM, MST. He produced 2 professors in India and Malaysia and 6 Ph.Ds by Feb 2016. Contact: firstname.lastname@example.org.
“Selective Maintenance Strategies for Complex Systems Under Uncertainty”
Prof. Yu Liu
University of Electronic Science and Technology of China
Bio: Yu Liu is a Professor in the School of Mechatronics Engineering, at the University of Electronic Science and Technology of China. He received his PhD degree in Mechatronics Engineering from the University of Electronic Science and Technology of China in 2010. He was a Visiting Pre-doctoral Fellow in the Department of Mechanical Engineering at Northwestern University, Evanston, U.S.A. from 2008 to 2010, and a Postdoctoral Research Fellow in the Department of Mechanical Engineering, at the University of Alberta, Edmonton, Canada from 2012 to 2013. His research interests include system reliability modeling and analysis, maintenance decisions, prognostics and health management, and design under uncertainty. He has published over 50 peer-reviewed papers in international journals and conferences. He serves as the reviewer of IEEE Transactions on Reliability, Journal of Mechanical Design-Transactions of The ASME, Structural and Multidisciplinary Optimization, Reliability Engineering and System Safety, etc. He was a recipient of the HIWIN Doctoral Dissertation Award sponsored by HIWIN Technologies Corporation and Chinese Society of Mechanical Engineers.
“Probabilistic Fatigue Life Prognosis for Steel Railway Bridges after Local Inspection and Repair”
Prof. Young Joo Lee
Ulsan National Institute of Science and Technology (UNIST)
Bio: Young Joo Lee received his B.S. and M.S. in civil engineering from Seoul National University, Korea, and a Ph.D. in civil engineering from the University of Illinois at Urbana-Champaign, USA. Dr. Lee joined Ulsan National Institute of Science and Technology (UNIST), Korea, in 2012 as research assistant professor in the School of Urban and Environmental Engineering, and he has been working as assistant professor in the same school since 2013.
Dr. Lee has research interests in various research areas including structural reliability, system reliability, network reliability, post-hazard loss prediction, and risk-based structural design and maintenance. In addition, he is conducting several research projects funded by the National Research Foundation of Korea, the Ministry of Land, Infrastructure and Transport of Korea, Korea Railway Research Institute, and Korea Institute of Civil Engineering and Building Technology. He is also a member of the American Society of Civil Engineers (ASCE), the Korean Society of Civil Engineers (KSCE), the Earthquake Engineering Society of Korea (EESK), the Korean Society of Hazard Mitigation (KOSHAM), and many others.
Dr. Lee teaches graduate and undergraduate courses in the areas of engineering risk & uncertainty and structural engineering, such as Probability Concepts in Engineering, Numerical Modeling and Analysis, Structural Reliability, Finite Element Method, and Random Vibration.
"Data-Driven Based Battery Health Prognosis with Diagnosis Uncertainties and Insufficient Training Data Sets"
Prof. Zhimin Xi
University of Tennessee - Knoxville
Bio: Zhimin Xi is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Tennessee - Knoxville. He received his B.S. and M.S. degree in Mechanical Engineering at the University of Science and Technology Beijing in 2001 and 2004, respectively. He obtained his Ph.D. in Reliability Engineering at the University of Maryland - College Park in 2010. His research interests include system reliability analysis, design optimization under uncertainty, prognostics and health management of engineering systems, model validation under uncertainty, human factors in system safety and reliability, and design of engineering resilient systems. He is the two-time winners of the Best Paper Award from ASME - Design Automation Conference (DAC) in 2008 and 2013, respectively. He received the Young Faculty Award (YFA) from the Defense Advanced Research Projects Agency (DARPA) in 2016. His research is supported by National Science Foundation, Department of Energy, DARPA, Ford Motor Company, Denso North American Foundation, and The Woodbridge Group.
"Model- and Non-model-based Damage Detection Methods Using Vibration Data"
Prof. Weidong Zhu
University of Maryland Baltimore Country
Bio: Weidong Zhu is a Professor in the Department of Mechanical Engineering at the University of Maryland, Baltimore County, and the founder and director of its Dynamic Systems and Vibrations Laboratory and Laser Vibrometry Laboratory. He received his double major BS degree in Mechanical Engineering and Computational Science from Shanghai Jiao Tong University in 1986, and his MS and PhD degrees in Mechanical Engineering from Arizona State University and the University of California at Berkeley in 1988 and 1994, respectively. He is a recipient of the 2003 National Science Foundation CAREER Award, the 2007 American Society for Nondestructive Testing Fellowship Award, the 2008 ChangJiang Scholar Chair Professorship in General Mechanics from the Ministry of Education of China, and the 2009 Daily Record's Maryland Innovator of the Year Award. He has been an ASME Fellow since 2010 and was an Associate Editor of the ASME Journal of Vibration and Acoustics from 2007-2014. His research spans the fields of dynamics, vibration, control, applied mechanics, structural health monitoring, and wind energy, and involves analytical development, numerical simulation, experimental validation, and industrial application. He has published about 100 journal papers.
Prof. Weidong Zhu
University of Maryland Baltimore Country, USA
Model- and Non-model-based Damage Detection Methods Using Vibration Data
Department of Mechanical Engineering
University of Maryland, Baltimore County
Baltimore, MD 21250
Modal parameters of structures, such as natural frequencies and mode shapes, have been widely used for vibration-based structural damage detection. A model-based damage detection method that uses changes in natural frequencies to detect damage has advantages over conventional nondestructive tests in detecting various types of damage using minimum measurement data. Two major challenges associated with applications of the model-based method to practical engineering structures are addressed: accurate modeling of test structures and the development of a robust inverse algorithm to detect damage, which are defined as the forward and inverse problems associated with the damage detection method, respectively. To resolve the forward problem, new physics-based finite element modeling techniques for fillets in thin-walled beams and bolted joints are developed, so that complex structures with thin-walled beams and/or bolted joints can be accurately modeled with a reasonable model size. To resolve the inverse problem, a robust iterative algorithm using a trust-region method, called the Levenberg-Marquardt (LM) method is developed to accurately detect locations and extent of damage using a minimum number of measured natural frequencies. The LM method can ensure global convergence of iterations in solving severely under-determined system equations and deal with damage detection problems with relatively large modeling error and measurement noise. The vibration-based damage detection method developed is applied to various structures including lightning masts, a space frame structure, and a pipeline. The locations and extent of damage can be successfully detected in experimental damage detection. In the numerical simulation where there are no modeling error and measurement noise, exact locations and extent of damage can be detected.
Besides the model-based method, non-model-based methods that use vibration shapes to identify damage in beams and plates are introduced. Curvature mode shapes and continuous wavelet transforms of mode shapes of a damaged beam are compared with those from polynomial fits with proper orders to yield curvature damage indices and continuous wavelet transform damage indices, respectively, to identify damage. A non-model-based method that uses mode shapes and one that uses principal, mean and Gaussian curvature mode shapes are introduced to identify damage in plates. A new multi-scale differential geometry scheme is developed to calculate curvature mode shapes. Comparing a mode shape of a damaged plate with that from a polynomial that fits the mode shape can yield a mode shape damage index to identify damage, and comparing curvature mode shapes associated with a mode shape of a damaged plate with those from a polynomial that fits the mode shape can yield four curvature damage indices to identify damage. Two non-model-based methods that use vibration shapes measured by a continuously scanning laser Doppler vibrometer system are introduced to identify damage in beams. Spatially detailed vibration shapes can be measured by the system in a rapid and accurate manner. Curvature damage indices can be obtained using curvature vibration shapes from the demodulation method to identify damage. All of the above non-model-based methods are robust against measurement noise and do not require any a priori information of undamaged structures that are usually not available in practice.
Prof. Zhimin Xi
University of Tennessee - Knoxville, USA
Data-Driven Based Battery Health Prognosis with Diagnosis Uncertainties and Insufficient Training Data Sets
This paper investigates data-driven based battery prognosis with diagnosis uncertainties and insufficient training data sets. Four types of data-driven prognosis methods are investigated including the neural network, similarity-based approach, relevance vector machine, and a recently developed copula-based approach. The remaining useful life (RUL) predictions of lithium-ion battery capacity are compared with capacity estimation error due to the fact that onboard lithium-ion battery capacity estimation is difficult and almost always contains estimation errors. Thus, robustness of each prognosis methods can be studied for real time capacity RUL estimation. Furthermore, collection of sufficient run-to-failure training data sets for lithium-ion batteries is almost impossible even though it is desirable for all data-driven based methods. Therefore, robustness of these methods in terms of the insufficient training data sets is also studied. These insightful results will help designers choose appropriate prognosis algorithms in designing battery management systems (BMS) for lithium-ion batteries.
Prof. Qingkai Han
Dalian University of Technology, China
Health Monitoring and Vibratory Fault Prediction of Rotating Machinery
The major rotating machinery including large centrifugal or axial flow compressor, gas turbine and aero-engine are in the value chains of high-end and the core aspects of the industry factories, regarded as important embodiments of the national industrial core competence and high-technical level. These machines in industrial plant as key equipment are very expensive costing a few million dollars, and a single day's loss of shutdown may be very huge. Maintenance is of high importance but very difficult even many researchers and companies have made a lot of efforts and contributions.
However, there is no comprehensive and effective technology to completely solve the problem until now. Recently, maintenance programs for major rotating machinery are developing into preventive maintenance and predictive maintenance. In order to truly implement the preventive or predictive maintenance on them, several advanced and practical technologies, i.e. mostly associated with health monitoring and fault prediction, are prompted to be broken. On the other hand, the various indicators used to study the health of the machine, especially to deal with vibratory faults often occurred on the machine, are predominantly vibratory related; after all, any change in the condition of the machine affects its dynamic conditions and therefore the vibratory behaviors.
The health monitoring and fault prediction of rotating machinery include the following aspects: 1) health monitoring strategy and fault prediction principles; 2) fault mechanisms of rotor systems and structures; 3) advanced measurement technologies; 4) advanced signal data processing technologies; 5) vibration fault detection and diagnosis; 6) fault prediction and life estimation.
Taking the aero-engine rotor system and its blades as examples, several vibratory faults are presented to reveal new mechanism and give new vibration behaviors, such as blade rubbing-impact, rotor cavity oil induced instability, elastic-supporting misalignment and high order resonance induced fatigue of blade. Optical fiber-Bragg sensors and wireless strain transducers are introduced into the measurements of bearing deformations or blade cracks. The obtained vibration signals of machine and structure are processed to extract the feature parameters to indicate sensitively the healthy or fault conditions by using of time and/or frequency domain analyses. The fault diagnoses are classified as data driven and model based, either statistical or artificial intelligent ones. At last, the main difficulty of fault prediction lies in the evolution process of a fault and the happening time of failure, and the estimation of fault remaining life length. Both evolution speed based and state model based prediction technologies are investigated by using an example of bearing damage fault.
Some successful examples and cases are introduced. The most important contribution is to identify what is truly effective for practical plant maintenance among these proposed technologies.
Prof. Nam-Ho Kim
University of Florida, USA
Physics-based prognostics-promises and challenges
Traditionally, prognostics algorithms have been developed in physics-based and data-driven approaches. Since the two approaches have their own pros and cons, it may not be informative to compare these two approaches. Instead, this presentation will focus on the characteristics of physics-based prognostics algorithms so that researchers can understand and interpret the outcomes of the algorithms better. The presentation starts with the discussions on how to select physics-based methods depending on model complexity, the number of available data, and the level of noise. Then, the presentation shows how to rank different prognostics algorithms when the true degradation model is not available. It will be shown that the rank of algorithms depends on the particular realization of noise. Since most physics-based algorithms are based on statistical inference, the presentation lastly addresses statistical correlation, which includes the correlation between model parameters, between bias and initial damage, and between model parameters and loading conditions. It is important to consider all these effects in order to properly use physics-based prognostics algorithms and properly interpret the outcomes.
Prof. Christian Gogu
Universite Toulouse III, France
New prognostics-based structural maintenance strategies for civil aircraft
Currently, structural maintenance of civil aircraft is based on the scheduled maintenance strategy, where all the aircraft of a same model are inspected according to a predetermined schedule. Embedding sensors in aeronautical structures allowing to monitor their structural health can also open up new strategies for more efficient maintenance planning. A first possibility is to move from scheduled maintenance to condition based maintenance, where the current condition of the aircraft triggers specific maintenance policies. A further step is to not only use the current state of the damage but to also predict future damage growth and determine the maintenance policy accordingly. Both condition based as well as prognostics based strategies can be carried out independently or can be coordinated with other, scheduled, non-structural maintenances, which leads to a total of four new possible maintenance strategies. An application of these maintenance strategies to fuselage panels of a short range commercial aircraft is presented and their effectiveness and cost-efficiency is compared between them and with that of traditional scheduled maintenance. We show that large savings can be achieved, particularly when large variabilities are present.
Prof. Bruno Castanier
University of Angers, France
What are the effects of the reliability model uncertainties in the maintenance decisions?
While the risk due to the quality and quantity of the available data is one of the major concerns in the product design and qualification processes, this issue seems not to be tackled in the operational phases and especially for the optimization of maintenance policies. Indeed, the vast majority of the works proposed in the literature concentrates on the definition of a maintenance rule for the maximization of a long-term economic profitability by assuming well-defined and stationary reliability or degradation models. However, the convergence to these stationary states can be really slow and strongly related to the knowledge level of the failure modes and therefore to the data collected during its operation. In order to reduce this uncertainty, it is necessary to integrate some of knowledge acquired during the various product qualification and endurance tests. This remains, from our point of view, one of the major areas of improvement in industrial practices, especially for the
development of the condition-based maintenance approaches.
The context of this work is the definition of optimization criteria which embrace the uncertainty on the reliability models and, in a longer term, to strengthen the relationships between the product design and qualification processes to the operating and maintenance phases. The focus of this paper is to highlight, based on numerical examples, the impact of the level of the available data on the efficiency of the maintenance decision, especially in condition-based maintenance, and, subsequently, to discuss on possible extensions in the maintenance decision criteria.
Prof. Jung Ryul Lee
Recent Advances In In-process NDE and Smart Hangar for Aerosapce SHM
Recently, we are in transition from metallic to composite in operation and manufacturing of aerospace structures. NDE, SHM, and PHM are all over used, in design, manufacturing, operation and overhau stepsl in their lifes. In this invited talk, opto-electronic systems capable of in-process NDE, in-situ NDE and integrated SHM and thier real world applications are introduced. In addition, their field implementation scenario are proposed and advances in Smart Hangar are introduced.
Prof. Chao Hu
Iowa State University, USA
Ensemble Learning with Degradation-Dependent Weights for Remaining Useful Life Prediction
Remaining useful life (RUL) prediction is critical to implement predictive maintenance. While significant research has been conducted in model-based and data-driven prognostics, very limited research has been done to investigate the prediction of RUL using an ensemble learning method that combines prediction results from multiple learning algorithms. The objective of this research is to introduce a new ensemble prognostics method with degradation-dependent weights. Specially, this method assigns an optimized, degradation-dependent weight to each learner (i.e., learning algorithm) such that the weighted sum of the prediction results from all the learners predicts the RUL of mechanical components with better accuracy. The ensemble prognostic algorithm is demonstrated using an experimental data collected from an engine simulator. Experimental results have shown that the predictive model trained by the ensemble learning algorithm outperform the existing methods.
Prof. Myong-Kee Jeong
Rutgers University, USA
Deep Learning Based Virtual Metrology (VM) and Yield Prediction in Semiconductor Manufacturing Processes
In this talk, we present the new deep learning based supervised autoencoder to extract meaningful features from massive in-line sensor functional signals of semiconductor manufacturing processes. Based on those extract features, we build the virtual metrology (VM) model to predict important quality characteristics of the process and the yield prediction model.
Mr. Mark Henss
University of Stuttgart, Germany
Framework for a Uniform Description of Prognostics and Health Management
The successful application of Prognostics and Health Management (PHM) systems increase world-wide steadily due to an increase of smart products on the market. Today PHM systems are developed and applied in different engineering disciplines using different models and methods. The diversity regarding models, methods and types of products lead to highly complex systems. To handle the complexity in an efficient way there is a need for a framework of PHM systems.
This paper describes the mathematical framework of PHM based on a generic model and a clear formalization (notation and semantic). The model includes modeling, diagnostics, prognostics and optimization. By the kind of application (e.g. the used lifetime method) the characteristics of the model are defined. Lifetime methods to estimate the Remaining Useful Life (RUL) are separated from the models to make the framework manageable for all disciplines. Combined with probability mathematics the uncertainties in PHM systems (model, prognostics, RUL etc.) are shown.
On the one hand, a common notation, semantic and a clear framework is necessary to manage highly complex systems. On the other hand, the cost and benefit of a PHM system are connected to the uncertainties. Both points are presented in this paper as the base to choose the right PHM system design.
Prof. Young Joo Lee
Ulsan National Institute of Science and Technology (UNIST),
Probabilistic Fatigue Life Prognosis for Steel Railway Bridges after Local Inspection and Repair
Steel railway bridges are exposed to repeated train loads which often cause fatigue failure. To guarantee the target fatigue life, bridge maintenance such as local inspection and repair should be properly provided based on accurate fatigue life prognosis, but it is a challenging task because there are various sources of uncertainty associated with bridges, train loads, environment, and maintenance work. For the optimal risk-based maintenance, it is thus essential to predict the probabilistic fatigue life of a steel railway bridge and update the life prognosis information based on the results of local inspection and repair. In this research, a probabilistic approach is proposed to estimate the fatigue failure risk of steel railway bridges and update the prior information of fatigue life prognosis after bridges are inspected and repaired. The proposed method is applied to a generic steel railway bridge, and the effects of local inspection and repair on the probabilistic fatigue life prognosis is discussed through parametric studies.
Dr. Miso Kim
Korea Research Institute of Standards and Science,
Metamaterial-based Enhancement of Elastic Wave Energy Harvesting
Enhancement of metamaterial-based energy harvesting will be presented from design, analysis towards experimental demonstration. Metamaterials, artificially engineered materials, exhibit unique properties including bandgap and negative refractive index and thus enable us to manipulate mechanical wave propagations. In order to amplify input mechanical wave energy into energy harvesting systems, metamaterials can be utilized to guide and localize acoustic and elastic waves towards the desired position for harvesting. Recently, several research efforts on metamaterial-based enhancement of energy harvesting have been reported, but mostly based on intuitive design or with little experimental support. We propose several metamaterial-based energy harvesting systems including phononic crystals with defect and acoustic metamaterials with local resonances. Systematic design through geometric and bandgap optimization process is performed and followed by experimental demonstration. Drastic enhancement of energy harvesting performance via metamaterials is demonstrated and thoroughly investigated both analytically and experimentally.
Prof. Byeng D. Youn
Seoul National University, Korea
PHM Frontiers: Physics-based Approach to Deep Learning
Journal bearing rotor systems are widely used in various industrial applications. Due to uncertainties, the rotor may not behave as it is expected, and thus accidents may happen, which causes economic losses. To prevent such incidents, diagnosing the health state of rotor systems is of critical importance. Conventional health diagnosis approaches heavily rely on the physical features of the rotor systems, which require large amount of resources to develop. To minimize the amount of time and effort required for the feature engineering process of the rotor systems, the deep learning based feature engineering method is proposed in this paper.
Among various deep learning techniques, convolutional neural networks (CNN), which is known to be powerful in recognizing images, is used. To use CNN, vibration signals are transformed into images using the omni-directional regeneration (ODR) method. The ODR method can generate virtual vibration signals in any direction from the actual vibration signals acquired from the two orthogonally placed proximity sensors. Then, the virtual ODR signals are transformed into images that sketch vibration signals acquired at all directions. Lastly, the image patterns are recognized by CNN, which replaces the expensive feature engineering step used in the conventional methods. The proposed deep learning based feature engineering is validated with the datasets from the journal bearing rotor testbed. It is demonstrated that the deep learning based anomaly detection of the journal bearings can substantially improve the efficiency and accuracy, compared to the conventional anomaly detection methods.
Prof. Diego Galar
Lulea University of Technology, Sweden
Maintenance Analytics, industrial data science and virtual commissioning
Industrial systems are complex with respect to technology and operations with involvement in a wide range of human actors, organizations and technical solutions. For the operations and control of such complex environments, a viable solution is to apply intelligent computerized systems, such as computerized control systems, or advanced monitoring and diagnostic systems. Moreover, assets cannot compromise the safety of the users by applying operation and maintenance activities. Industry 4.0 is a term that describes the fourth generation of industrial activity which is enabled by smart systems and Internet-based solutions. Two of the characteristic features of Industry 4.0 are computerization by utilizing cyber-physical systems and intelligent factories that are based on the concept of "internet of things". Maintenance is one of the application areas, referred to as maintenance 4.0, in form of self-learning and smart systems that predicts failure, makes diagnosis and triggers maintenance by making use of "internet of things".
Thus, for complex assets, much information needs to be captured and mined to assess the overall condition of the whole system including the one from design and manufacturing which obviously contains the physical knowledge. Therefore the integration of asset information during the entire lifecycle is required to get an accurate health assessment of the whole system, and determine the probability of a shutdown or slowdown avoiding black swans and other unexpected or unknown asset behaviors.
Moreover, the asset data are not only huge but often dispersed across independent systems that are difficult to access, fuse and mine due to disparate nature and granularity. If the data from these independent systems are combined into a common correlated data source, these new sets of information will add value to the individual data sources.
This talk will discuss the possibilities that lie within applying the maintenance analytics concept by the means of virtualization i.e virtual commissioning of the assets through data fusion and integration from a systems perspective.