"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
"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
"Intelligent failure prognostics of lithium-ion energy storage and renewable energy systems" Prof. Chao Hu, Iowa State University, USA
Prof. Myong-Kee Jeong, Rutgers University, USA
Prof. Hyung-Jo Jung, KAIST, Korea
"Physics-based prognostics-promises and challenges" Prof. Namho Kim, University of Florida, USA
"Recent Advances In In-process NDE and Smart Hangar for Aerosapce SHM" Prof. Jung Ryul Lee, KAIST, Korea
Prof. Yu Liu, University of Electronic Science and Technology of China, China
Prof. Sankaran Mahadevan, Vanderbilt University, USA
Prof. Pingfeng Wang, Wichita State University, USA
"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
"Framework for a Uniform Description of Prognostics and Health Management" Prof. Peter Zeiler, Stuttgart University, Germany
“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”
Prof. 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.
“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.
“Intelligent failure prognostics of lithium-ion energy storage and renewable energy systems”
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.
“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)
“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.
"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.