Prof. Gautam Biswas – Vanderbilt University, Nashville, USA
Gautam Biswas holds an Endowed Chair – he is a Cornelius Vanderbilt Professor of Computer Science and Computer Engineering, in the EECS Department and a Senior Research Scientist at the Institute for Software Integrated Systems (ISIS) at Vanderbilt University. He has an undergraduate degree in Electrical Engineering from the Indian Institute of Technology (IIT) in Mumbai, India, and M.S. and Ph.D. degrees in Computer Science from Michigan State University in E. Lansing, MI. Currently, Prof. Biswas is the lead on the VISOR (Vanderbilt Initiative for Smart cities Operations and Research) TIPS center at Vanderbilt University. Prof. Biswas conducts research in Intelligent Systems with primary interests in hybrid modeling, simulation, and analysis of complex embedded systems, and their applications to diagnosis, prognosis, and fault-adaptive control. More recently, he is working on data mining for diagnosis, and developing methods that combine model-based and data-driven approaches for diagnostic and prognostic reasoning. This work, in conjunction with Honeywell Technical Center and NASA Ames, received the NASA 2011 Aeronautics Research Mission Directorate Technology and Innovation Group Award for Vehicle Level Reasoning System and Data Mining methods to improve aircraft diagnostic and prognostic systems. In other research projects, he is involved in developing simulation-based environments for learning and instruction. His research has been supported by funding from NASA, NSF, DARPA, and the US Department of Education. He has over 600 refereed publications, and is a Fellow of the IEEE, a Prognostics and Health Management (PHM) Society Fellow, and member of the ACM, AAAI, AIED, and the Sigma Xi Research Societies.
Combining Data-Driven and Model-Based Methods to Improve Diagnosis of Complex Systems
ABSTRACT: Traditional methods for model-based diagnosis assume an equation-based or a graphical model can be constructed to represent system behavior. This model forms the basis for designing fault detection and isolation schemes. However, as systems have become more complex, and with the advent of embedded and cyber physical systems (CPS), it is becoming harder to construct models of systems that are sufficiently complete and accurate to support correct diagnoses. On the other hand, recent advances in sensor technology in conjunction with the computing revolution has enabled significant scaling of data collection, data storage, and data processing schemes, leading researchers and practitioners to now turn their attention to data-driven methods for fault detection and isolation. In this talk, I will outline different ways in which data (especially large amounts of operational data) can be used by itself or combined with models to achieve more accurate and more complete diagnosis systems. After presenting a framework for combining model- and data-driven methods for diagnosis, I will present a number of case studies from work we have done in enhancing model-based diagnosers with data-driven methods. Then I will discuss our more recent work in using unsupervised learning methods for anomaly detection, i.e., finding previously undetected faults from large volumes of operational data collected from complex systems. I will end the talk by providing interesting directions for future research.
Prof. Mattias Nyberg – KTH Royal Institute of Technology, Stockholm, Sweden
Mattias Nyberg is an adjunct (part-time) professor at Royal Institute of Technology in the department of Mechatronics. His main affiliation is however Scania CV AB, a leading global heavy truck manufacturer, where he is currently a technical manager of “functional safety”. He received a PhD in Electrical Engineering from Linköping University in 1999 specializing in vehicular systems and automotive diagnosis. After dissertation he has worked mainly in industry; first for Daimler in Stuttgart, Germany, with diesel engine diagnosis, and later at Scania with on- and off-board diagnosis, but lately also with functional safety . In parallel with his industrial career, he has maintained a strong research interest, and has been the main supervisor of six PhD students in the area of diagnosis and functional safety. He is an author of about 100 scientific publications, and received recently the SAE Vincent Bendix award for the best paper of year 2015 in the area of automotive electronics engineering.
Fault Diagnosis in the Automotive Industry
ABSTRACT: Fault diagnosis in automotive vehicles was introduced with the computerization of automotive control, mainly in the 80s. Since then, each new emission regulation has introduced more and more stringent requirements on the on-board diagnosis. In addition to engine control, today’s vehicles have computerized control, and therefore also on-board diagnosis, of almost all functions. As suggested by the academic field already in the 90s, model based methods is nowadays a standard solution for on-board diagnosis. Also off-board diagnosis has received much attention. The more and more complex vehicular systems have resulted in advanced computer-supported off-board diagnosis used by mechanics in the workshops. Current focuses in automotive industry are connectivity and ADAS (Advanced Driver Assistance Systems). The potential of having all vehicles connected to internet opens up for new solutions of remote diagnosis; i.e. to allow the mechanics to read out diagnosis related information and do troubleshooting while the vehicle is on the road. ADAS functions make the on-board computers safety critical, which implies that the on-board diagnosis gets high requirements on the detection of safety critical faults. This is also regulated by the new ISO26262 standard. The trend goes towards even more complex and safety critical vehicles, much driven by the vision of self-driving vehicles. To enable troubleshooting of these vehicles in the future, there will be a strong need for certified safety critical on-board diagnosis and computerized and automated off-board guided diagnosis and troubleshooting.
Dr. Hong Wang – Pacific Northwest National Laboratory, Richland, USA
Hong Wang (M’97–SM’02) received the B.S. degree from the Huainan University of Mining Engineering, Huainan, China, in 1982, and the M.S. and Ph.D. degrees from the Huazhong University of Science and Technology, Wuhan, China, in 1984 and 1987, respectively. He was a Research Fellow with Salford University, Salford, U.K., Brunel University, Uxbridge, U.K., and Southampton University, Southampton, U.K., before joining the University of Manchester Institute of Science and Technology, Manchester, U.K., in 1992, and had been a Professor in process control of complex industrial systems from 2002 to 2016. He has been with Pacific Northwest National Laboratory, Richland, WA, USA, as a Laboratory Fellow and Chief Scientist since 2016. He has authored over 200 papers and three books in these areas. His current research interests include stochastic distribution control, fault detection and diagnosis, nonlinear control, and data-based modeling for complex systems. He originated the work on stochastic distribution control (SDC), where the main purpose of control input design is to make the shape of the output probability density functions to follow a targeted function for general non-Gaussian dynamic systems. Dr. Wang was an Associate Editor of the IEEE Transactions on Automatic Control. He is currently serves as an Associate Editor of the IEEE Transactions on Control Systems Technology and the IEEE Transactions on Automation Science And Engineering.
Collaborative Fault Tolerant Control for Complex Industrial Processes: Present and Future
ABSTRACT: Operation of complex industrial processes involves raw material supplies chain, material processing lines and after-sales services. Examples are steel making, mineral processing, power systems, car manufacturing, papermaking and petro-chemical plants, where in the production phase there are a lot of control systems working collaboratively to fulfill the required production. These control systems have multiple layered interface with the on-site human operators at different level and the whole system works horizontally through various stages of material processing along production lines and vertically through the integration of different layers of loop control, operational control, planning and scheduling as well as operational management. During the operation of these processes, safety is an important issue that ensures the achievement for optimized product quality and production efficiency. Since for such complex systems there are a lot of subsystems working together in a collaborative way, a challenging issue would be how the system can still operate safely when some fault occur in the system. Moreover, if a subsystem has a fault can other subsystems actively participate in a collaborative way so as to achieve fault tolerant control? In this plenary talk, the state-of-the-art on collaborative fault tolerant control schemes for complex industrial processes will be described. This is then followed by the description of operational fault tolerant control that realizes the fault tolerance functionality at operational level in response to the loop control faults and the recently cascaded collaborative fault tolerant control for stochastic distribution systems. Some examples will be given as well to demonstrate these algorithms together with future directions.
Prof. Christopher Edwards – University of Exeter, UK
Christopher Edwards is Professor of Control Engineering in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter, UK. He began his academic career in the department of Engineering at the University of Leicester as a Lecturer in 1996. He is currently a member of the IEEevE technical committee on Variable Structure Systems and a subject editor for the International Journal of Robust and Nonlinear Control. In 2006, Prof. Edwards was awarded a Royal Academy of Engineering Senior Research Fellowship. His current research interests are in sliding mode control and observation, and their application to fault detection and fault tolerant control problems. He is the author of over 350 refereed papers in these areas, and seral books including: Sliding mode control: theory and applications (1998), and Fault Detection and Fault Tolerant Control using Sliding Modes (2011). In addition he recently co-edited the monograph Fault Tolerant Flight Control: a Benchmark Challenge (2010) based on GARTEUR AG16.
Fault Tolerant Control using Sliding Modes
ABSTRACT: Sliding modes in dynamical systems have been historically studied because of their strong robustness properties to a certain class of uncertainty. In feedback control systems, this is achieved by employing nonlinear control signals to force the system trajectories to attain, in finite time, a motion along a surface in the state-space. The associated reduced order dynamics the system exhibits, whilst constrained to the surface, is called the sliding motion. This motion possesses strong robustness properties to so-called matched uncertainty. This talk will argue that actuator faults quite naturally fall into this class and therefore sliding mode controllers inherently possess fault tolerance. Furthermore careful integration of sliding mode ideas within a control allocation framework will be shown to have the capability of dealing with faults and total failures in over-actuated systems. Several different architectures will be described based on conventional and integral sliding modes. The practicality of these methods will be demonstrated using examples of applications of these ideas applied to aerospace systems. The talk will also include results from recent piloted flight tests of a sliding mode fault tolerant controller undertaken as part of the H2020/Japan collaborative project VISION.
Prof. Gang Niu – Tongji University, China
Dr. Gang Niu is an associate professor at Tongji University in Shanghai, China. He graduated from Beihang University in China, and received his Ph.D. in mechatronics engineering at Pukyong National University in South Korea. Prior to joining Tongji, he spent several years in China Aero-polytechnology Establishment (CAPE) and City university of Hong Kong, focusing on complex system health management, testability design and validation. His current research interests are in the fields of hybrid diagnostics and prognostics, and autonomous health management for intelligent vehicle systems. He has published over 50 articles mainly found in international journals like Mechanical Systems and Signal Processing, Reliability Engineering and System Safety, Expert Systems with Applications, Structural Health Monitoring, etc. with citation over 400 by researchers over 20 countries. He is the chief author of two books published by Springer-Verlag and Science Press Beijing. In the past five years, he served as Technical Committee and session chair in several international conferences like IEEE-PHM, WCEAM, and QR2MSE. He is the editorial board member of IMechE Part C: Journal of Mechanical Engineering Science and IEEE Senior Member.
Fault Detection Diagnosis and Prognosis towards Autonomous Health Management and Maintenance Optimization for Rail Vehicle Systems
ABSTRACT: Over the past several years, a paradigm shift has occurred in engineering fields where complex systems are presenting more characters of hybrid, interaction, dynamic, data-rich and multi-energy transformation. Prognostics and Health Management (PHM) is essential in guaranteeing the safe, efficient, and correct operation of complex of detection, isolation and identification of faults; and prognosis, which consists of prediction of the remaining useful life (RUL) of components, subsystems, or systems; constitute systems health monitoring. PHM aims to provide users with an integrated view of the health state of equipment or overall system. An effective PHM system is expected to provide early and isolation of the precursor and/or incipient fault of detection components or sub-elements; to have the means to monitor and predict the progression of the fault; and to aid in making, or autonomously trigger maintenance schedule and asset management decisions or actions. This talk will introduce the Infrastructure of PHM platform and its technology approaches. In particular, the design & development procedure of engineering PHM system will be explained. Then cases study and ongoing related technique research in rail vehicle systems will be provided. The output of PHM technology will produce additional benefits to further research development towards PHM enabled autonomous health management that are the extension of fault diagnosis and prognostics to control reconfiguration and maintenance optimization.
Prof. Marios M. Polycarpou – University of Cyprus, Nicosia, Cyprus
Dr. Lothar Seybold – RAFI GmbH, Berg, Germany
Lothar Seybold is the VP and Member of the Board of Directors since 2015. He started his career 29 years ago with ABB Inc. in Switzerland, were remote software becomes first time an important issue for big electric dives operated in huge mills in the mining business all over the world. His first position was to develop a remote monitoring and diagnosis software for ABBs 28 MW GMD class. In 1999, he move to Integrated Systems Inc. in Germany to take over the position as Chief Technology Officer. His responsibility was development of a new model based monitoring and control software system for the manufacturing industry. In 2006, he joined the RAFI GmbH & Co. KG Company in Berg, Germany to take over the position as Head of Product- and Innovation management to coordinate and rapidly extent RAFIs capability for Innovation. In 2012, he took over the position as Directory R&D where he started the transformation of the company to an IoT-company. Since 2015, he is leading the business units ‘Systems’ and ‘Components’ and was also called into the Board of Directors to represent Technology and Innovation.
Transformation of Business – IoT as the New Souce of Innovation in Diagnosis and Control
ABSTRACT: The information technology is revolutionizing products. Products and services are merging in the Internet of Things (IoT). In order to accelerate this process furthermore, leading economic countries are promoting national IoT activities like Industrie 4.0. in Germany or Made in China 2025. This has unleashed a new era of competition. In this context, also the domain of diagnosis and control face a dramatic change in their industry. The traditional companies are forced to move away from the stand-alone diagnosis and control packages and suites to seamless integrate into new ecosystems. The services of the Global Players like Microsoft, Amazon, Google, … or big industrial providers like Bosch, GE or KUKA are the future breeding ground for their business. All this changes opens up an all-new field of innovation in the domain of diagnosis and control. The benefit of huge resources of data will for example move the topic of ‘sensor fusion’ into a new dimension. These elements, completed by a lot of others, will be broached and detailed during the presentation, with a strong industrial point of view, allowing the academia to better understand how the new businesses are created and what the engineering needs are.
Prof. Marcin Witczak – University of Zielona Góra, Poland
Prof. Ali Zolghadri – University of Bordeaux, France
Ali Zolghadri received his Ph.D. in 1992 from the University of Bordeaux – France, and has been a professor in Control & System Engineering there since 2003. He is a distinguished professor since 2014. His research deals with various aspects of control engineering, notably fault diagnosis, fault-tolerant control & guidance, and operational autonomy of complex systems. Between 2001 and 2015, he had been head of ARIA team, IMS-lab, CNRS-Bordeaux University. He has authored and co-authored over 75 papers in leading international journals, about 130 communications in international conferences, one Springer book and 12 book chapters. He is a co-holder of 14 patents (French and US) in the aerospace field. Dr. Zolghadri has been coordinator of a number of collaborative French, European and international research projects and actions in control and aeronautics. He is member of TC “Aerospace” and “Safeprocess” of IFAC, member of Council of European Aerospace Societies and SAE International. He has served as a program committee member and international advisory board member for various international conferences and has given many invited keynotes, plenary talks and seminaries during international events. He is member of executive committee of France’s Aerospace Valley Cluster and received an award for excellence in 2010 from the French Aeronautics and Space Foundation. In 2016, he received the CNRS Innovation Medal for outstanding scientific research with innovative applications in the technological and societal fields.
FDIR for Aerospace and Flight-critical Systems: Turning Theory into Practice
ABSTRACT: The aviation and aerospace industry is a powerful engine of innovation as it has to meet more and more aggressive performance targets in reliability, efficiency, safety, weight, range, environmental impact and emissions, etc. The challenges today are far greater than those faced in the past and continue to grow as individual systems evolve and operate with greater autonomy and intelligence within a networked and cyber-physical environment. Flight-critical applications provide numerous grounds where advanced FDIR techniques are needed to support conventional industrial practices. On the other hand, modern control theory offers a huge number of various designs, techniques and methods related to model-based FDIR and fault tolerant Control & Guidance. However, today, we have to recognize that the assessment is not overly enthusiastic in terms of real-world applications. The main focus of this talk is on a number of practical design considerations that should go along with any model-based FDIR design in order to provide a viable technological solution. Such considerations are decisive for the survivability of the design during ground/flight Validation & Verification (V&V) activities. The views reported in this paper are based on lessons learnt and results achieved through actions undertaken with Airbus during the last decade. One of the model-based monitoring methods that the author developed with Airbus received certification on new generation A350 aircraft and is flying since January 2015.