Ron J. Patton

Robust model-based fault diagnosis for dynamic systems

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.

Issues of Fault Diagnosis for Dynamic Systems

There is an increasing demand for dynamic systems to become safer, more reliable and more economical in operation. This requirement extends beyond the normally accepted safety-critical systems e.g., nuclear reactors, aircraft and many chemical processes, to systems such as autonomous vehicles and some process control systems where the system availability is vital. The field of fault diagnosis for dynamic systems (including fault detection and isolation) has become an important topic of research. Many applications of qualitative and quantitative modelling, statistical processing and neural networks are now being planned and developed in complex engineering systems. Issues of Fault Diagnosis for Dynamic Systems has been prepared by experts in fault detection and isolation (FDI) and fault diagnosis with wide ranging experience.Subjects featured include: - Real plant application studies; - Non-linear observer methods; - Robust approaches to FDI; - The use of parity equations; - Statistical process monitoring; - Qualitative modelling for diagnosis; - Parameter estimation approaches to FDI; - Fault diagnosis for descriptor systems; - FDI in inertial navigation; - Stuctured approaches to FDI; - Change detection methods; - Bio-medical studies. Researchers and industrial experts will appreciate the combination of practical issues and mathematical theory with many examples. Control engineers will profit from the application studies.

Sliding mode observers for fault detection and isolation

This paper considers the application of a particular sliding mode observer to the problem of fault detection and isolation. The novelty lies in the application of the equivalent output injection concept to explicitly reconstruct fault signals. Previous work in the area of fault detection using sliding mode observers has used disruption of the sliding motion to detect faults. A design procedure is described and nonlinear simulation results are presented to demonstrate the approach.

Design of unknown input observers and robust fault detection filters

Fault detection filters are a special class of observers that can generate directional residuals for the purpose of fault isolation. This paper proposes a new approach to design robust (in the disturbance de-coupling sense) fault detection filters which ensure that the residual vector, generated by this filter, has both robust and directional properties. This is done by combining the unknown input observer and fault detection filter principles. The paper proposes a new full-order unknown input observer, and gives necessary and sufficient conditions for its existence. After the disturbance de-coupling conditions are satisfied, the remaining design freedom can be used to make the residual have the directional property, based on the fault detection filter principle. A nonlinear jet engine system is used to illustrate the robust fault isolation approach presented. It is shown that linearization errors can be approximately treated as unknown disturbances and be de-coupled in the design of a robust fault detect...

Fault-Tolerant Control: The 1997 Situation

Abstract The fault-tolerant control problem belongs to the domain of complex control systems in which inter-control-disciplinary information and expertise are required. This paper outlines the state of the art in a field which remains largely a theoretical topic with most application studies based upon aerospace systems. The directions in which the subject is going are summarised and some pointers are given as to the likely future issues and where new research effort is required. The paper provides a basic literature review covering most areas of fault-tolerant control.

Robust Model-Based Fault Diagnosis: The State of the ART

Abstract The robustness issues in model-based fault detection and fault isolation (fault diagnosis) have received considerable attention in recent years, due to the increasing demand for safe and reliable operation of uncertain and complex dynamic systems. The ultimate goal of robustness is to provide rapid and reliable detection and isolation of system faults when the plant under control is disturbed, and when the mathematical model upon which the diagnosis is based cannot faithfully reproduce the full dynamic operation of the plant. The aim of this paper is to review methods for robust fault diagnosis, based principally on residual generation. Some of the key challenges and potential for future directions in the research are drawn up.

A Review of Parity Space Approaches to Fault Diagnosis

Abstract This paper reviews the state of the art in fault detection and isolation for dynamic systems, based on the parity space concept. Some important definitions are provided and tutorial examples are given to illustrate the theory. The main aim has been to draw together the important links between parity space approaches; in particular the important links between open-loop and closed-loop strategies. The parity space is used as a new re-statement to unify residual generation methods. The robustness and isolation problems in fault diagnosis is mainly addressed in the paper. Moreover, an important definition for robustness is made and the recent works on robust fault diagnosis methods are described. Some new design methods are given to illustrate the potential of robust residual generation using closed-loop parity space ideas.

Review of parity space approaches to fault diagnosis for aerospace systems

This paper provides a tutorial review of the state of the art in parity space fault diagnosis approaches with particular emphasis on aerospace systems. The basic concepts and definitions are given and a consistent framework is presented to draw together the important links amongst the known methods for fault diagnosis. Residual generation in the parity space has been recognized as a core element in this framework. The robustness and isolation problems are the main focus of the paper. Recent research topics on robust fault diagnosis are outlined, and new ideas as to how the parity space approach can be used to deal with robustness are discussed.

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

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.<<ETX>>

From differential equations to PDC controller design via numerical transformation

This paper proposes a transformation method capable of transforming analytically given differential equations of dynamic models into Takagi-Sugeno fuzzy inference model (TS fuzzy model), whereupon various parallel distributed compensation (PDC) controller design techniques can readily be executed. Joining the transformation method and the PDC techniques leads to a controller design framework. The transformation method is specialized to minimize the number of fuzzy rules in the resulting TS fuzzy model according to a given acceptable transformation error, the PDC design thus results in a computational complexity minimized controller which is highly desired in many cases of real applications. The paper presents examples to show the effectiveness of the proposed transformation.