Details

<p>Basic requirements on fault detection and estimation.- Basic methods for fault detection and estimation in static and dynamic processes.- Feedback control, observer, and residual generation.- Fault detection and estimation for linear time-varying systems.- Detection and isolation of multiplicative faults in uncertain systems.- Analysis, parameterisation and optimal design of nonlinear observer-based fault detection systems.- Data-driven fault detection methods for large-scale and distributed systems.- Alternative test statistics and data-driven fault detection methods.-&nbsp; Application of randomised algorithms to assessment and design of fault diagnosis systems.- Performance-based fault-tolerant control.- Performance degradation monitoring and recovering.- Data-driven fault-tolerant control schemes.</p>

<b>Prof. Dr.-Ing. Steven X. Ding</b> is head of the institute Automatic Control and Complex Systems (AKS) at the University of Duisburg, Germany. He received the Ph.D. degree in electrical engineering from the University of Duisburg in 1992. Between 1992 and 1994, he worked with Rheinmetall GmbH, Germany. From 1995 to 2001, he was professor of control engineering at the University of Applied Science Lausitz in Senftenberg, Germany, and was the the vice president of this university during 1998 – 2000. Since 2001, he has been a full professor of control engineering at the University of Duisburg-Essen. Prof. Ding has published three books and over 350 book contributions, journal and conference papers in the areas of model-based and data-driven fault diagnosis, process monitoring and control as well as their applications to the automotive, process and renewable energy industries.<div><br></div><div>Detailed information is available at AKS-website: http://aks.uni-due.de/htm/index.php?lang=en</div><div><br></div>

<div><p>After the first two books have been dedicated to model-based and data-driven fault diagnosis respectively, this book addresses topics in both model-based and data-driven thematic fields with considerable focuses on fault-tolerant control issues and application of machine learning methods. The major objective of the book is to study basic fault diagnosis and fault-tolerant control problems and to build a framework for long-term research efforts in the fault diagnosis and fault-tolerant control domain. In this framework, possibly unified solutions and methods can be developed for general classes of systems. The book is composed of six parts. Besides Part I serving as a common basis for the subsequent studies, Parts II - VI are dedicated to five different thematic areas, including model-based fault diagnosis methods for linear time-varying systems, nonlinear systems and systems with model uncertainties, statistical and data-driven fault diagnosis methods, assessment of fault diagnosis systems, as well as fault-tolerant control with a strong focus on performance degradation monitoring and recovering. These parts are self-contained and so structured that they can also be used for self-study on the concerned topics.</p>

<p><b>The content</b></p>

<p>Basic requirements on fault detection and estimation – Basic methods for fault detection and estimation in static and dynamic processes – Feedback control, observer, and residual generation – Fault detection and estimation for linear time-varying systems – Detection and isolation of multiplicative faults in uncertain systems – Analysis, parameterisation and optimal design of nonlinear observer-based fault detection systems – Data-driven fault detection methods for large-scale and distributed systems – Alternative test statistics and data-driven fault detection methods – Application of randomised algorithms to assessment and design of fault diagnosis systems – Performance-based fault-tolerant control – Performance degradation monitoring and recovering – Data-driven fault-tolerant control schemes</p>

<p>&nbsp;<b>The target groups</b></p>

<p>This book would be valuable for graduate and PhD students as well as for researchers and engineers in the field.</p>

<b>The author</b> Prof. Dr.-Ing. Steven X. Ding is a professor and the head of the Institute for Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Germany. His research interests are model-based and data-driven fault diagnosis, control and fault-tolerant systems as well as their applications in industry with a focus on automotive systems, chemical processes and renewable energy systems.<p></p><br></div>

The book describes the integrated application of model-based, data-driven and statistic learning methods It helps to understand advanced and integrated design as well as online optimization methods for fault diagnosis and fault-tolerant control in complex systems It describes engineering tools to solve fault diagnosis and fault-tolerant control problems