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.

Robust fault detection using eigenstructure assignment: a tutorial consideration and some new results

Abstract: Developments in the eigenstructure assignment approach to robust fault detection are discussed. By suitable assignment of the eigenstructure of an observer, the residual signal is decoupled from disturbances. The main contribution of this work is the novel use of right eigenvector assignment of observers, which gives more freedom for achieving disturbance decoupling. It is shown that, when decoupling conditions are satisfied, the resulting deadbeat design is equivalent to the first-order parity space structure for residual generation. Two tutorial examples are presented to illustrate the disturbance decoupling property and the conditions under which left or right eigenvectors are assignable. >

Robust fault detection of jet engine sensor systems using eigenstructure assignment

Abstract: This paper examines a robust fault detection scheme that can be used to detect faulty sensors of jet engines. The fault detection scheme has to be insensitive to disturbances while being highly sensitive to sensor faults (robust). The paper presents a complete description of a robust fault detection approach based on eigenstructure assignment, both in continuousand discrete-time domains. By assigning the left (or right) eigenvectors of the observer orthogonal (or parallel) to the disturbance directions, the robust (disturbance decoupling) fault detection is achieved. The approach has been applied to a realistic jet engine simulation system. The system is a 17th-order system, and a reduced-order model is used to approximate the system. Modeling errors are considered as disturbances acting on the fault detection scheme. A particularly novel feature of the work is the development and use of a new method (new in this context) for estimating disturbance direction. The robust fault detection scheme design uses this estimated direction as that of the direction of unknown inputs (disturbances). Simulation results show that the scheme can detect soft or incipient faults efficiently.

Optimal filtering and robust fault diagnosis of stochastic systems with unknown disturbances

Abstract: The paper studies the optimal filtering and robust fault diagnosis problems for stochastic systems with unknown disturbances. An optimal observer is proposed, which can produce disturbance decoupled state estimation with minimum variance for time varying systems with both noise and unknown disturbances. The existence conditions and the observer design procedure are presented. The output estimation error with disturbance decoupling and minimum variance properties is used as a residual signal. A statistical testing procedure is applied to examine the residual and is used to diagnose faults. The method developed is applied to an illustrative example, and simulation results show that the optimal observer can give good state estimation; the fault detection approach taken is able to detect faults reliably in the presence of both modelling errors and noise.

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