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Ron J. Patton

Other affiliations: Universities UK, York University, University of York  ...read more
Bio: Ron J. Patton is an academic researcher from University of Hull. The author has contributed to research in topics: Fault detection and isolation & Robustness (computer science). The author has an hindex of 57, co-authored 351 publications receiving 19210 citations. Previous affiliations of Ron J. Patton include Universities UK & York University.


Papers
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Book ChapterDOI
01 Jan 1999
TL;DR: The most important issue of reliable system operation is to detect and isolate incipient faults as early as possible to give the operator enough information and time to take proper measures to prevent any serious consequence on the system.
Abstract: To ensure reliable operation of control systems, hard faults in system components are not tolerable and must be detected before they actually occur. Hopefully, faults are detected during the maintenance stage. However, the situation is different for soft (incipient) faults. Their effect on the system is very small and almost unnoticeable during their incipient stage. They may develop slowly to cause very serious effects on the system, although these incipient faults may be tolerable when they first appear. Hence, the most important issue of reliable system operation is to detect and isolate incipient faults as early as possible. An early indication of incipient faults can give the operator enough information and time to take proper measures to prevent any serious consequence on the system.

1 citations

Proceedings ArticleDOI
02 Jul 2007
TL;DR: This paper investigates the development of a new type of dynamic neuro-fuzzy system with neuronal rules and its application to fault detection and isolation (FDI) of components of a dynamic process.
Abstract: This paper investigates the development of a new type of dynamic neuro-fuzzy system with neuronal rules and its application to fault detection and isolation (FDI) of components of a dynamic process. Hybrid learning based on fuzzy clustering algorithm and the steepest-descent method, is used to train the proposed neuro-fuzzy system. The experimental case study concerns the component fault diagnosis of a three-tank system. A neuro-fuzzy simplified observer scheme is used to generate the residuals (symptoms) in the form of one step-ahead prediction errors. These are then analysed by a neural classifier in order to take the appropriate decision regarding the actual process behaviour.

1 citations

Journal ArticleDOI
TL;DR: In this article, the authors outline the theory of a design approach to the detection of faults in dynamic systems using the concept of the robust unknown input observer and provide an interpretation of the process of robust residual generation together with the underlying principles and concepts.

1 citations

Journal ArticleDOI
TL;DR: A bank of fuzzy observers is used to detect faults and to isolate failed sensors and the fuzzy observer driven by healthy sensors provides reliable torque and flux estimates which are used for the control purpose.
Abstract: This paper studies the fault diagnosis and fault-tolerant estimation problems of a rail traction system. The main idea is to use a bank of fuzzy observers to detect faults and to isolate failed sensors. Once the failed sensor is isolated, the fuzzy observer driven by healthy sensors provides reliable torque and flux estimates which are used for the control purpose. The concept and design procedures for fuzzy observers are presented in this paper. A fuzzy observer is able to produce accurate state estimations and generate robust residuals for non-linear dynamic systems such as a rail traction system. The fault diagnosis and fault-tolerant estimation scheme proposed for the rail traction system is demonstrated using simulations.

1 citations

Journal ArticleDOI
TL;DR: In this article, state observers are designed for the benchmark of a three-tank water process and it is shown that based on single or double measurements the missing water level can be reconstructed.

1 citations


Cited by
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Journal ArticleDOI
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

14,635 citations

Journal ArticleDOI
TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.

3,848 citations

Journal ArticleDOI
TL;DR: A unified framework for the design and the performance analysis of the algorithms for solving change detection problems and links with the analytical redundancy approach to fault detection in linear systems are established.
Abstract: This book is downloadable from http://www.irisa.fr/sisthem/kniga/. Many monitoring problems can be stated as the problem of detecting a change in the parameters of a static or dynamic stochastic system. The main goal of this book is to describe a unified framework for the design and the performance analysis of the algorithms for solving these change detection problems. Also the book contains the key mathematical background necessary for this purpose. Finally links with the analytical redundancy approach to fault detection in linear systems are established. We call abrupt change any change in the parameters of the system that occurs either instantaneously or at least very fast with respect to the sampling period of the measurements. Abrupt changes by no means refer to changes with large magnitude; on the contrary, in most applications the main problem is to detect small changes. Moreover, in some applications, the early warning of small - and not necessarily fast - changes is of crucial interest in order to avoid the economic or even catastrophic consequences that can result from an accumulation of such small changes. For example, small faults arising in the sensors of a navigation system can result, through the underlying integration, in serious errors in the estimated position of the plane. Another example is the early warning of small deviations from the normal operating conditions of an industrial process. The early detection of slight changes in the state of the process allows to plan in a more adequate manner the periods during which the process should be inspected and possibly repaired, and thus to reduce the exploitation costs.

3,830 citations

Book
27 Sep 2011
TL;DR: Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research.
Abstract: There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world

3,826 citations

Journal ArticleDOI
TL;DR: In this article, the authors review the state of the art of fault detection and isolation in automatic processes using analytical redundancy, and present some new results with emphasis on the latest attempts to achieve robustness with respect to modelling errors.

3,313 citations