Tutorials

One of the unique features of the PHM conferences is free technical tutorials on various topics in health management taught by industry experts. At the Second European PHM conference, tutorials will take place on Tuesday, July 8, and Wednesday, July 9. As educational events tutorials provide a comprehensive introduction to the state-of-the-art in the tutorial’s topic. Proposed tutorials address the interests of a varied audience: beginners, developers, designers, researchers, practitioners, and decision makers who wish to learn a given aspect of prognostic health management. Tutorials will focus both on theoretical aspects as well as industrial applications of prognostics. These tutorials reach a good balance between the topic coverage and its relevance to the community.

Tutorial Topics

  • Consistency-Based Diagnosis
    Presented by: Dr. Anibal Bregon, Assistant Professor in Computer Science with the Department of Computer Science, University of Valladolid, Spain
  • Particle Filters for Prognostics
    Presented by: Dr. Piero Baraldi, Assistant Professor of Nuclear Engineering at the department of Energy at the Politecnico di Milano, Italy
  • Cyber-Physical Systems and Big Data Analytics for PHM Transformation
    Presented by: Dr. Jay Lee, Professor at the University of Cincinnati, USA
  • Electronics PHM
    Presented by: Dr. Jose Celaya, Research Scientist, SGT Inc. at the Prognostics Center of Excellence, NASA Ames Research Center, USA, and

    Dr. Abhinav Saxena, Research Scientist, SGT Inc., at the Prognostics Center of Excellence, NASA Ames Research Center, USA

Key Conference Dates
Tutorials 08-09 Jul 2014


PHM Conference tutorials have been a popular event in the past and the PHM society is proud to continue this service to the community. Topics of interest of these tutorials span fundamentals of PHM (Diagnostics, Prognostics, Health Management, Uncertainty Management, etc.) as well as specialized topics such as Cost-Benefit analysis, Data-Mining, Electronics PHM, Bayesian Filtering for Prognosis, etc. For a more comprehensive list of past tutorials please look at the following links:

Past PHM Tutorials
[PHM 2009] [PHM 2010] [PHM 2011] [PHM 2012] [PHME 2012] [PHM 2013]


Tutorials Chair:
Kai Goebel kai.goebel@nasa.gov

Tutorial Details

Tutorial Title: Consistency-Based Diagnosis
Anibal Bregon, Assistant Professor in Computer Science with the Department of Computer Science, University of Valladolid, Spain.
Abstract:The need for increased performance, safety, and reliability of complex engineering systems motivates the development of efficient fault diagnosis methodologies. Fault diagnosis is fundamental to reduce downtime and increase system availability through the life of the system. The process of fault diagnosis includes timely fault detection, quick fault isolation, and accurate fault identification. The focus of this tutorial is on model-based approaches to on-line fault detection, isolation, and identification (FDII) in complex dynamic systems. An advantage of using model-based techniques against other diagnosis approaches, like expert systems or machine learning, lies in the re-usability of models and the diagnostic algorithms. In particular, this tutorial will focus on the consistency approach to model-based diagnosis (known as consistency-based diagnosis, CBD), which has seen significant research activities from the Artificial Intelligence diagnosis (DX) community in the last two decades.
Presenter Bio: Dr. Anibal Bregon is an Assistant Professor and research assistant in Computer Science with the Department of Computer Science, University of Valladolid, Spain. Dr. Bregon received his B.Sc., M.Sc., and Ph.D. degrees in Computer Science from the University of Valladolid, Valladolid, Spain, in 2005, 2007, and 2010, respectively. He has carried out both basic and applied research in the areas of fault diagnosis and prognosis for aerospace and industrial systems, has co-authored more than 60 peer-reviewed papers, and has participated on several national funded projects on fault diagnosis and prognosis topics. Dr. Bregon has been a guest researcher with the Diagnostics and Prognostics Group, NASA Ames Research Center, Moffett Field, CA, USA, with the Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA, and with the Department of Electrical Engineering, Linkoping University, Linkoping, Sweden. His current research interests include model-based reasoning for diagnosis, prognosis, health-management, and distributed diagnosis and prognosis of complex physical systems. Dr. Bregon is a member of the Prognostics and Health Management Society and the IEEE. Among various other professional activities, he holds different chair positions at the PHM and PHME conferences, and is a Technical Program Committee member of the Prognostics and Health Management conference and the International Workshop on Principles of Diagnosis.



Tutorial Title: Particle Filters for Prognostics
Dr. Piero Baraldi, Assistant Professor of Nuclear Engineering at the department of Energy at the Politecnico di Milano, Italy
Abstract: Model-based prognostic methods use an explicit mathematical model of the degradation process to predict the future evolution of the degradation state and, thus, the RUL of the system. In practice, it is sometimes difficult to obtain an accurate RUL estimate since the degradation state of the system may not be directly observable and/or the measurements may be affected by noise and disturbances. In these cases, model-based estimation methods can be used to infer the dynamic degradation state and provide a reliable quantification of the estimation uncertainty on the basis of the sequence of available noisy measurements. Many approaches rely on Bayesian methods such as the exact Kalman filter, the Extended Kalman filter, Gaussian-sum filters, or the grid-based filters. Numerical approximations based on the Monte Carlo sampling technique, such as the Particle Filtering (PF) have gained popularity for their flexibility and ease of design. The aim of the tutorial is to illustrate the development and use of PF-based methods for the prediction of the Remaining Useful Life of a degrading component.
The first part of this tutorial will be devoted to the illustration of the theoretical basis of the PF method and its application to the prediction of the RUL of a degrading component.
In the second part of the tutorial, we will show different applications of the PF method to prognostics. The applications have differing degrees of knowledge about component behavior, and include different industrial components, such as structures, turbine blades, electric capacitors degrading, and nuclear power plant seawater.
Presenter Bio: Dr. Piero Baraldi is assistant professor of Nuclear Engineering at the department of Energy at the Politecnico di Milano (Italy). He received his BS in nuclear engng., Politecnico di Milano, 2002 and PhD in nuclear engng., Politecnico di Milano, 2006. He is the current chairman of the European Safety and Reliability Association, ESRA, Technical Committee on Fault Diagnosis. He is functioning as Technical Committee Co-chair of the European Safety and Reliability Conference, ESREL 2014, and he has been the Technical Programme Chair of the 2013 Prognostics and System Health Management Conference (PHM-2013). He is serving as editorial board member of the international scientific journals: “Journal of Risk and Reliability” and “International Journal on Performability Engineering”. His main research efforts are currently devoted to the development of methods and techniques (neural networks, fuzzy and neuro-fuzzy logic systems, ensemble system, kernel regression methods, clustering techniques, genetic algorithms) for system health monitoring, fault diagnosis, prognosis and maintenance optimization. He is also interested in methodologies for rationally handling the uncertainty and ambiguity in the information. He is co-author of 59 papers on international journals, 55 on proceedings of international conferences and 2 books. He serves as referee of 4 international journals.



Tutorial Title: Cyber-Physical Systems and Big Data Analytics for PHM Transformation
Dr. Jay Lee is Ohio Eminent Scholar, L.W. Scott Alter Chair Professor, and Distinguished University Professor at the University of Cincinnati, USA
Abstract: In today’s competitive business environment, companies are facing challenges in dealing with big data issues for rapid decision making for improved productivity. Many manufacturing systems are not ready to manage big data due to the lack of smart analytics tools. Germany is leading a transformation toward 4th Generation Industrial Revolution (Industry 4.0) based on Cyber-Physical System based manufacturing and service innovation. As more software and embedded intelligence are integrated in industrial products and systems, predictive technologies can further intertwine intelligent algorithms with electronics and tether-free intelligence to predict product performance degradation and autonomously manage and optimize product service needs,

The presentation will address the trends of industrial transformation in big data environment as well as the readiness of smart predictive informatics tools to manage big data to achieve transparency and productivity. First, industry transformation including Germany 4.0 and cyber-physical system will be introduced. Second, advanced prognostics technologies for smart product service systems with case studies will be presented. In addition, research advances in designing cyber-physical modeling for next-generation PHM will be discussed.

Presenter Bio: Dr. Jay Lee is Ohio Eminent Scholar, L.W. Scott Alter Chair Professor, and Distinguished University Professor at the University of Cincinnati and is founding director of National Science Foundation (NSF) Industry/University Cooperative Research Center (I/UCRC) on Intelligent Maintenance Systems (IMS) which is a multi-campus NSF Industry/University Cooperative Research Center which consists of the University of Cincinnati (lead institution), the University of Michigan, Missouri University of S&T, and the University of Texas-Austin. Since its inception in 2001, the Center has been supported by over 80 global companies. His current research focuses on intelligent prognostics and predictive analytics. He has authored/co-authored numerous highly influential articles and technical papers in the areas of machinery monitoring and prognostics, E-manufacturing, and intelligent maintenance systems. He has over 20 patents and trademarks. He is a Fellow of ASME, SME, as well as a founding fellow of International Society of Engineering Asset Management (ISEAM).



Tutorial Title: Electronics PHM
Dr. Jose Celaya, Research Scientist, SGT Inc. at the Prognostics Center of Excellence, NASA Ames Research Center, USA, and

Dr. Abhinav Saxena, Research Scientist, SGT Inc., at the Prognostics Center of Excellence, NASA Ames Research Center, USA

Abstract: The focus of this tutorial is on the area of prognostics of electronic systems. An overview of the state of the art will be presented. A review of the most common methods based on the data-driven prognostics approach will be presented. Special attention will be given to model-based prognostics methods for electronic components like power transistors and filter capacitors. This will include modeling of the degradation processes and how to make use of such models in order to assess the condition-based state of health and to predict remaining useful life. A discussion on how these model-based approaches relate to the electronics reliability tools will be presented making emphasis on difference and similarities but with the main objective of conveying how such approaches complement each other.
Presenter Bio: José R. Celaya is a senior research scientist with SGT Inc. at the Prognostics Center of Excellence, NASA Ames Research Center. He received a Ph.D. degree in Decision Sciences and Engineering Systems in 2008, a M. E. degree in Operations Research and Statistics in 2008, a M. S. degree in Electrical Engineering in 2003, all from Rensselaer Polytechnic Institute, Troy New York; and a B. S. in Cybernetics Engineering in 2001 from CETYS University, México.


Abhinav Saxena is a senior research scientist with SGT Inc. at the Prognostics Center of Excellence NASA Ames Research Center, Moffett Field, CA. His research involves developing prognostic algorithms and methodologies to standardize prognostics that include performance evaluation and requirement specification for prognostics of engineering systems. He has been involved in PHM research for the last seven years and has published several papers on these topics. He is a PhD in Electrical and Computer Engineering from Georgia Institute of Technology, Atlanta. He earned his B.Tech. in 2001 from Indian Institute of Technology (IIT) Delhi, and a Masters Degree from Georgia Tech in 2003.
  
 
 
 

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