Fault Diagnosis of Dynamic Systems: Quantitative and Qualitative Approaches

ISBN-13: 9783030177270 / Angielski / Twarda / 2019 / 462 str.

Teresa Escobet; Anibal Bregon; Belarmino Pulido

Opis

Software developed using free distribution license or in MATLAB that allow the students develop some of the exercises/examples proposed in the book. Models developed in MATLAB that are proposed as case studies/examples in the book.

Spis treści

Introduction and Fundamental Concepts.- Case Studies.- Part I: Standard Approaches.- Structural Analysis.- The FDI Approach.- The DX Approach.- Bridge: Integration of FDI and DX Approaches.- Data-Driven Fault Diagnosis.- Discrete-Event Systems Fault Diagnosis.- Part II: Advanced Approaches.- Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems.- Model-Based Diagnosis with Probabilistic Models.- Qualitative Modeling for Fault Diagnosis.- Model Based Fault Diagnosis of Hybrid Systems.- Model-Based Software Debugging.- Diagnosing Business Processes.- Prognosis.

O autorze

Prof. Teresa Escobet received her B.sc./M.sc. Degree in Industrial Engineering at UPC in 1989 and PhD at the same University in 1997. She began work at UPC as an Assistant Prof. in 1986 and she earned the status of Associate Prof. in 2001. Her teaching activities are related to issues of Automatic Control. She is a member of the research group “Advanced Control Systems (SAC)”. Her main research interests are in dynamic system modelling and identification applied to fault detection, isolation, fault-tolerant control and condition-based maintenance. She has been involved in several International and national research projects and networks, and she has published more than 130 journal and conference papers and she has supervised 6 PhD dissertations. She has been NOC member of the 3rd IEEE Conference on Control and Fault-Tolerant Systems (Systol 2016).

Dr. Anibal Bregon received his B.Sc., M.Sc. and Ph.D. degrees in Computer Science from the University of Valladolid (Spain) in 2005, 2007 and 2010, respectively. He joined the Department of Computer Science at the University of Valladolid in 2011, where he is Associate Professor since February 2018. 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 80 journal and conference papers, and has participated on several funded projects, networks and contracts on fault diagnosis and prognosis topics, and on Big Data analytics. He has been guest researcher with the Intelligent Systems Division at NASA Ames Research Center and the Institute for Software Integrated Systems at Vanderbilt University, among others. His current research interests include model-based reasoning for diagnosis and prognosis, health-management, Big Data and Industry 4.0. Among various other professional activities, he has held different chair positions at the PHM and PHME conferences, has been IPC member of several conferences, such as DX and PHM, has been co-administrator of several courses and summer schools on diagnosis, prognosis, and artificial intelligence, and has been the Local Chair of the 2016 European Conference of the Prognostics and Health Management Society.

Prof. Belarmino Pulido received his Engineering degree, M.Sc. degree, and Ph.D. degree in Computer Science from the University of Valladolid, Valladolid, Spain, in 1992, 1995, and 2001 respectively. In 1994 he joined the Department of Computer Science at the University of Valladolid, where he is Associate Professor since 2002. His main research interests are Model-based reasoning and Knowledge-based reasoning, and their application to Supervision and Diagnosis. Currently he is working in model-based diagnosis and prognosis of distributed hybrid systems. He is the coordinator of the Spanish Network on Supervision and Diagnosis of Complex Systems since 2005. He has worked in different regional (3) national (16) and European/international (3) funded projects related to Supervision and Diagnosis, being the main researcher in 6. He has been the supervisor of 2 PhD Theses and 2 MsC Theses on model-based diagnosis, has published 26 papers in JCR journals/publications, 9 papers on non JCR journals, 53 international and 24 national conference papers. He has been IPC member on serveral conferences such as DX or ECAI. He organized DX-06. Moreover, he is regular reviewer for different journals related with AI and diagnosis: IEEE Trans. on Syst. Man and Cybernetics (with different name changes), AI Comm, Eng. Apps. of Artificial Intelligence, etc. and also reviewer for IFAC, Safeprocess or ECC conferences.

Prof. Vicenç Puig received a Telecommunications Engineering B.sc./M.sc. Degree in 1993 and a PhD degree in Automatic Control, Vision, and Robotics in 1999, both from Universitat Politècnica de Catalunya (UPC). He is Full Professor of the Automatic Control Department (ESAII) and Researcher of Institut de Robòtica i Informàtica Industrial (IRI) at UPC. He is currently the Director of the Automatic Control Department since 2015 and the Head of the research group on advanced control systems (SAC) since 2007 at UPC. He has developed important scientific contributions in the areas of fault diagnosis and fault tolerant control using interval and linear-parameter-varying models with set-based approaches. He has participated in more than 20 European and national research projects in the last decade. He has also led many private contracts with several companies and has published more than 100 journal articles as well as 450 papers in international conference/workshop proceedings. He has supervised over 21 PhD dissertations and more than 40 Master’s theses/final projects. He is currently the Vice-chair of the IFAC Safeprocess TC Committee 6.4 since 2014. He has been the Chair of the 3rd IEEE Conference on Control and Fault-Tolerant Systems (Systol 2016) and the IPC Chair of IFAC Safeprocess 2018. He is also Associate Editor of the International Journal of Robust and Non-linear Control and ISA Transactions.

Blurb

Fault Diagnosis of Dynamic Systems provides readers with a glimpse into the fundamental issues and techniques of fault diagnosis used by Automatic Control (FDI) and Artificial Intelligence (DX) research communities. The book reviews the standard techniques and approaches widely used in both communities. It also contains benchmark examples and case studies that demonstrate how the same problem can be solved using the presented approaches. The book also introduces advanced fault diagnosis approaches that are currently still being researched, including methods for non-linear, hybrid, discrete-event and software/business systems, as well as, an introduction to prognosis.

Fault Diagnosis of Dynamic Systems is valuable source of information for researchers and engineers starting to work on fault diagnosis and willing to have a reference guide on the main concepts and standard approaches on fault diagnosis. Readers with experience on one of the two main communities will also find it useful to learn the fundamental concepts of the other community and the synergies between them. The book is also open to researchers or academics who are already familiar with the standard approaches, since they will find a collection of advanced approaches with more specific and advanced topics or with application to different domains. Finally, engineers and researchers looking for transferable fault diagnosis methods will also find useful insights in the book.