Observer-Based Fault Estimation and Accomodation for Dynamic Systems


Book Description

Due to the increasing security and reliability demand of actual industrial process control systems, the study on fault diagnosis and fault tolerant control of dynamic systems has received considerable attention. Fault accommodation (FA) is one of effective methods that can be used to enhance system stability and reliability, so it has been widely and in-depth investigated and become a hot topic in recent years. Fault detection is used to monitor whether a fault occurs, which is the first step in FA. On the basis of fault detection, fault estimation (FE) is utilized to determine online the magnitude of the fault, which is a very important step because the additional controller is designed using the fault estimate. Compared with fault detection, the design difficulties of FE would increase a lot, so research on FE and accommodation is very challenging. Although there have been advancements reported on FE and accommodation for dynamic systems, the common methods at the present stage have design difficulties, which limit applications of respective design approaches. Therefore, the problems of FE and accommodation are needed to be further studied. This book considers the theory and technology of FE and accommodation for dynamic systems, and establishes a systemic and comprehensive framework of FE and accommodation for continuous/discrete-time systems.




Observer-Based Fault Estimation Techniques


Book Description

This book investigates observer-fault estimation techniques in detail, while also highlighting recent research and findings regarding fault estimation. Many practical control systems are subject to possible malfunctions, which may cause significant performance loss or even system instability. To improve the reliability, performance and safety of dynamical systems, fault diagnosis techniques are now receiving considerable attention, both in research and applications, and have been the subject of intensive investigations. Fault detection – the essential first step in fault diagnosis – is a binary decision-making process used to determine whether or not a fault has occurred. In turn, fault isolation is used to identify the location of the faulty component, while fault estimation is used to identify the size of the fault online. Compared with the problems involved in fault detection and isolation, fault estimation is considerably more challenging.




Robust Model-Based Fault Diagnosis for Dynamic Systems


Book Description

There is an increasing demand for dynamic systems to become more safe and reliable. This requirement extends beyond the normally accepted safety-critical systems of nuclear reactors and aircraft where safety is paramount important, to systems such as autonomous vehicles and fast railways where the system availability is vital. It is clear that fault diagnosis (including fault detection and isolation, FDI) has been becoming an important subject in modern control theory and practice. For example, the number of papers on FDI presented in many control-related conferences has been increasing steadily. The subject of fault detection and isolation continues to mature to an established field of research in control engineering. A large amount of knowledge on model-based fault diagnosis has been ac cumulated through the literature since the beginning of the 1970s. However, publications are scattered over many papers and a few edited books. Up to the end of 1997, there is no any book which presents the subject in an unified framework. The consequence of this is the lack of "common language", dif ferent researchers use different terminology. This problem has obstructed the progress of model-based FDI techniques and has been causing great concern in research community. Many survey papers have been published to tackle this problem. However, a book which presents the materials in a unified format and provides a comprehensive foundation of model-based FDI is urgently needed.







Robust Observer-Based Fault Diagnosis for Nonlinear Systems Using MATLAB®


Book Description

This book introduces several observer-based methods, including: • the sliding-mode observer • the adaptive observer • the unknown-input observer and • the descriptor observer method for the problem of fault detection, isolation and estimation, allowing readers to compare and contrast the different approaches. The authors present basic material on Lyapunov stability theory, H¥ control theory, sliding-mode control theory and linear matrix inequality problems in a self-contained and step-by-step manner. Detailed and rigorous mathematical proofs are provided for all the results developed in the text so that readers can quickly gain a good understanding of the material. MATLAB® and Simulink® codes for all the examples, which can be downloaded from http://extras.springer.com, enable students to follow the methods and illustrative examples easily. The systems used in the examples make the book highly relevant to real-world problems in industrial control engineering and include a seventh-order aircraft model, a single-link flexible joint robot arm and a satellite controller. To help readers quickly find the information they need and to improve readability, the individual chapters are written so as to be semi-independent of each other. Robust Oberserver-Based Fault Diagnosis for Nonlinear Systems Using MATLAB® is of interest to process, aerospace, robotics and control engineers, engineering students and researchers with a control engineering background.




Model-Based Fault Diagnosis


Book Description

This book investigates in detail model-based fault diagnosis methods, including observer-based residual generation, residual evaluation based on threshold computation, observer-based fault isolation strategies, observer-based fault estimation, Kalman filter-based fault diagnosis methods, and parity space approach. Studies on model-based fault diagnosis have attracted engineers and scientists from various disciplines, such as electrical, aerospace, mechanical, and chemical engineering. Pursuing a holistic approach, the book establishes a fundamental framework for this topic, while emphasizing the importance of state-space approach. The methods introduced in the book are systemic and easy to follow. The book is intended for undergraduate and graduate students who are interested in fault diagnosis and state estimation, researchers investigating fault diagnosis and fault-tolerant control, and control system design engineers working on safety-critical systems.




Observer-Based Fault Diagnosis and Fault-Tolerant Control for Switched Systems


Book Description

This book focuses on the fault diagnosis observer design for the switched system. Model-based fault diagnosis and fault tolerant control are one of the most popular research directions in recent decades. It contains eight chapters. Every chapter is independent in the method of observer design, but all chapters are around the same topic. Besides, in each chapter, the model description and theoretical results are firstly provided, then some practical application examples are illustrated to prove the obtained results. The advanced theoretical methodologies will benefit researchers or engineers in the area of safety engineering and the arrangement of the structure will help the readers to understand the content easily.




Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques


Book Description

Safety in industrial process and production plants is a concern of rising importance but because the control devices which are now exploited to improve the performance of industrial processes include both sophisticated digital system design techniques and complex hardware, there is a higher probability of failure. Control systems must include automatic supervision of closed-loop operation to detect and isolate malfunctions quickly. A promising method for solving this problem is "analytical redundancy", in which residual signals are obtained and an accurate model of the system mimics real process behaviour. If a fault occurs, the residual signal is used to diagnose and isolate the malfunction. This book focuses on model identification oriented to the analytical approach of fault diagnosis and identification covering: choice of model structure; parameter identification; residual generation; and fault diagnosis and isolation. Sample case studies are used to demonstrate the application of these techniques.




Fault Diagnosis and Fault-Tolerant Control Based on Adaptive Control Approach


Book Description

This book provides recent theoretical developments in and practical applications of fault diagnosis and fault tolerant control for complex dynamical systems, including uncertain systems, linear and nonlinear systems. Combining adaptive control technique with other control methodologies, it investigates the problems of fault diagnosis and fault tolerant control for uncertain dynamic systems with or without time delay. As such, the book provides readers a solid understanding of fault diagnosis and fault tolerant control based on adaptive control technology. Given its depth and breadth, it is well suited for undergraduate and graduate courses on linear system theory, nonlinear system theory, fault diagnosis and fault tolerant control techniques. Further, it can be used as a reference source for academic research on fault diagnosis and fault tolerant control, and for postgraduates in the field of control theory and engineering.




Fault-Diagnosis Systems


Book Description

With increasing demands for efficiency and product quality plus progress in the integration of automatic control systems in high-cost mechatronic and safety-critical processes, the field of supervision (or monitoring), fault detection and fault diagnosis plays an important role. The book gives an introduction into advanced methods of fault detection and diagnosis (FDD). After definitions of important terms, it considers the reliability, availability, safety and systems integrity of technical processes. Then fault-detection methods for single signals without models such as limit and trend checking and with harmonic and stochastic models, such as Fourier analysis, correlation and wavelets are treated. This is followed by fault detection with process models using the relationships between signals such as parameter estimation, parity equations, observers and principal component analysis. The treated fault-diagnosis methods include classification methods from Bayes classification to neural networks with decision trees and inference methods from approximate reasoning with fuzzy logic to hybrid fuzzy-neuro systems. Several practical examples for fault detection and diagnosis of DC motor drives, a centrifugal pump, automotive suspension and tire demonstrate applications.