Advanced methods for fault diagnosis and fault-tolerant control

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.

The content

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

 The target groups

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


The author 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.