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

Robust Model-Based Fault Diagnosis for Dynamic Systems

Robustness in Model-Based Fault Diagnosis: The 1995 Situation

Robust Fault Detection and Isolation (FDI) Systems

To guarantee safe operation and mission completion, any fault in an automatic system has to be diagnosed as early as possible. Model-based techniques have been widely recognized as feasible and powerful approaches for diagnosing faults and require a mathematical model of the monitored system. A prerequisite for successful model-based fault diagnosis is satisfactory robustness with respect to modelling uncertainties. This thesis examines and develops further the theory and application of robust residual generation techniques in model-based fault diagnosis, beginning with a study and review of basic principles of model-based fault diagnosis. A number of strategies for the design of robust residual generators are then proposed. The thesis proposes a new full-order unknown input observer structure for robust residual generation and this structure is then used to design directional and minimum variance residuals. This is followed by a very thorough presentation of the eigenstructure assignment approach to fault diagnosis. A new algorithm to assign right observer eigenvectors in disturbance de-coupling design is presented. The disturbance decoupling residual generation is then used for diagnosing faults in a jet engine system example. To facilitate this application, several techniques are proposed to derive an approximate disturbance distribution matrix. These techniques enlarge the application domain of disturbance de-coupling residual generation approaches. Robust residual generation can be treated as a multi-objective optimization problem in which fault sensitivity is to be maximized, whilst the sensitivity to modeffing uncertainties is to be minimized. The thesis defines a number of performance indices in observer-based residual generation and the multi-objective optimization is solved by a combination of the method of inequalities and genetic algorithms. Finally, the thesis studies the design of optimally robust parity relations using multi-criterion optimization. The techniques developed in this thesis are well illustrated using either academic or practical application examples and the results show the effectiveness of the developed techniques.

Robust residual generation for model-based fault diagnosis of dynamic systems.

Robust Model-Based Fault Diagnosis Of Chemical Process Systems. (November 2005) Srinivasan Rajaraman, B.S., Indian Institute of Technology, Roorkee, India Co–Chairs of Advisory Committee: M. Sam Mannan Juergen Hahn Fault detection and diagnosis have gained central importance in the chemical process industries over the past decade. This is due to several reasons, one of them being that copious amount of data is available from large number of sensors in process plants. Moreover, since industrial processes operate in closed loop with appropriate output feedback to attain certain performance objectives, instrument faults have a direct effect on the overall performance of the automation system. Extracting essential information about the state of the system and processing the measurements for detecting, discriminating, and identifying abnormal readings are important tasks of a fault diagnosis system. The goal of this thesis is to develop such fault diagnosis systems, which use limited information about the process model to robustly detect, discriminate, and reconstruct instrumentation faults. Broadly, the proposed method consists of a novel nonlinear state and parameter estimator coupled with a fault detection, discrimination, and reconstruction system.

Robust model-based fault diagnosis for chemical process systems

Design of optimal residuals for detecting sensor faults using multi-objective optimization and genetic algorithms

PP056 [Resuscitation, Stabilisation & Transport » Resuscitation]: EPIDEMIOLOGY OF CARDIOPULMONARY ARREST AND OUTCOME OF RESUSCITATION IN PICU ACROSS CHINA: A PROSPECTIVE MULTICENTER COHORT STUDY

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Design of optimal residuals for detecting sensor faults using multi-objective optimization and genetic algorithms

PP056 [Resuscitation, Stabilisation & Transport » Resuscitation]: EPIDEMIOLOGY OF CARDIOPULMONARY ARREST AND OUTCOME OF RESUSCITATION IN PICU ACROSS CHINA: A PROSPECTIVE MULTICENTER COHORT STUDY