Ron J. Patton

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.

Reliable fault diagnosis scheme for a spacecraft attitude control system

Abstract: This paper presents a scheme for fault detection and isolation (FDI) of on-board gyroscope sensors and thrusters for spacecraft attitude control, based on the example of the Mars Express (MEX) satellite. The main contribution of the paper is related to the design and the optimization of an FDI procedure based on robust observers or filters, used as estimators, which generate the FDI residual signals. When organized into an estimator bank, excellent fault isolation properties are achieved upon suitable design. The residual evaluation relies on decision logic, whose thresholds are properly selected and specified. The FDI strategy is applied to the non-linear simulation of the MEX system, and the FDI performance is evaluated subject to disturbance signals, model uncertainty, and measurement noise processes. The robustness and reliability properties of the robust residual generators are investigated and verified in simulation by selecting suitable performance criteria together with Monte Carlo analysis. The r...

Fault Diagnosis of Non-Linear Dynamic Systems

Abstract: The majority of model-based fault diagnosis methods are based on linear system models. For non-linear systems, the fault diagnosis problem has been traditionally approached in two steps. Firstly, the model is linearized at an operating point, and then robust techniques are applied to generate residual signals which are insensitive to model parameter variations within a small neighborhood of the operating point. The robustness issue is tackled using techniques developed for linear system models. The strategy only works well when the linearization does not cause a large mismatch between linear model and non-linear behavior, the residual has been designed to be robust enough to tolerate small model perturbations around the operating point, and the system closely operates around the operating point specified. However, for systems with high nonlinearity and a wide dynamic operating range, the linearized approach fails to give satisfactory results. A linearized model is an approximate description of the non-linear system dynamics around the operating point. However, when the system operating range becomes wider, the linearized model is no longer able to represent the system dynamics. One solution is to use a large number of linearized models corresponding to a range of operating points. However, this would involve a large number of FDI systems corresponding to all operating points. This is not very practical for real-time application.

Fault Diagnosis of an Electro-pneumatic Valve Actuator Using Neural Networks With Fuzzy Capabilities

Abstract: The early detection of faults (just beginning and still developing) can help avoid system shutdown, breakdown and even catastrophes involving human fatalities and material damage. Computational intelligence techniques are being investigated as an extension to the traditional fault diagnosis methods. This paper discusses the neuro-fuzzy approach to modelling and fault diagnosis, based on the TSK/Mamdani approaches. An application study of an electro-pneumatic valve actuator in a sugar factory is described. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are outlined.

Design of a low-sensitivity, minimum norm and structurally constrained control law using eigenstructure assignment

Abstract: The freedom for eigenvalue assignment in specified areas of the complex plane and of the scope for placing the corresponding eigenvectors is utilized to form an analytical gradient-based optimization problem which may be used to determine a low-sensitivity eigenvalue assignment with a structurally constrained and low-norm linear gain matrix. Examples are used to compare the properties of such optimized controllers with those obtained using single-objective design techniques.

Detection of abrupt changes: theory and application

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.

Robust Model-Based Fault Diagnosis for Dynamic Systems

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