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“Fault Detection and Isolation: Multi-Vehicle Unmanned System” deals with the design and development of fault detection and isolation algorithms for unmanned vehicles such as spacecraft, aerial drones and other related vehicles. Addressing fault detection and isolation is a key step towards designing autonomous, fault-tolerant cooperative control of networks of unmanned systems. This book proposes a solution based on a geometric approach, and presents new theoretical findings for fault detection and isolation in Markovian jump systems. Also discussed are the effects of large environmental disturbances, as well as communication channels, on unmanned systems. The book proposes novel solutions to difficulties like robustness issues, as well as communication channel anomalies. “Fault Detection and Isolation: Multi-Vehicle Unmanned System” is an ideal book for researchers and engineers working in the fields of fault detection, as well as networks of unmanned vehicles.
In this dissertation, a linear estimator for fault detection and isolation called the Game Theoretic Multiple-Fault Detection Filter is derived for both continuous and discrete systems. The detection filter uses a disturbance attenuation formulation to bound the transmission of disturbances to the output, approximately blocking all but one fault from each of a set of projected residuals. However, different from previous approximate methods for single-fault detection filters, the multiple-fault detection filter utilizes a secondary optimization problem to generate a solution for the estimator gain that achieves more advanced detection filter goals. Specifically, the current work examines an optimization that increases sensitivity of each projected residual to its target fault. For the continuous case, it is proven that the new detection filter approximates previous detection filters obtained from geometric and spectral theories and extends them to finite time-varying systems. Further, the detection filter is demonstrated via numerical examples.
This paper considers fault detection, isolation and reconfiguration (FDIR) for the localization sensors, including the dead reckoning and external sensors, of an autonomous ground vehicle (AGV) designed for use in highly unstructured outdoor environments. Ten sensors are considered in this research. None of these sensors are identical, but subsets of them do have the ability to measure or calculate (based on simple algebra) the same kinematical parameters. To improve the localization accuracy, selected sensor outputs are fused using Kalman filters. The fused data and selected sensor measurements are then combined into a set of linearly independent parity equations, which leads to the generation of a bank of residuals. A fault in any one of the ten sensors causes a unique subset of these residuals to grow, which allows the fault to be detected and isolated. This allows a control scheme based on these sensors to reconfigure itself so that only the non-faulty sensors are used for localization. The effectiveness of this FDIR scheme is demonstrated in the context of a recently developed algorithm for maneuvering an AGV in cluttered environments.
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.
These Proceedings provide a general overview as well as detailed information on the developing field of reliability and safety of technical processes in automatically controlled processes. The plenary papers present the state-of-the-art and an overview in the areas of aircraft and nuclear power stations, because these safety-critical system domains possess the most highly developed fault management and supervision schemes. Additional plenary papers covered the recent developments in analytical redundancy. In total there are 95 papers presented in these Proceedings.
This book focuses on unhealthy cyber-physical systems. Consisting of 14 chapters, it discusses recognizing the beginning of the fault, diagnosing the appearance of the fault, and stopping the system or switching to a special control mode known as fault-tolerant control. Each chapter includes the background, motivation, quantitative development (equations), and case studies/illustration/tutorial (simulations, experiences, curves, tables, etc.). Readers can easily tailor the techniques presented to accommodate their ad hoc applications.
Since the time our first book Fault Diagnosis in Dynamic Systems: The ory and Applications was published in 1989 by Prentice Hall, there has been a surge in interest in research and applications into reliable methods for diag nosing faults in complex systems. The first book sold more than 1,200 copies and has become the main text in fault diagnosis for dynamic systems. This book will follow on this excellent record by focusing on some of the advances in this subject, by introducing new concepts in research and new application topics. The work cannot provide an exhaustive discussion of all the recent research in fault diagnosis for dynamic systems, but nevertheless serves to sample some of the major issues. It has been valuable once again to have the co-operation of experts throughout the world working in industry, gov emment establishments and academic institutions in writing the individual chapters. Sometimes dynamical systems have associated numerical models available in state space or in frequency domain format. When model infor mation is available, the quantitative model-based approach to fault diagnosis can be taken, using the mathematical model to generate analytically redun dant alternatives to the measured signals. When this approach is used, it becomes important to try to understand the limitations of the mathematical models i. e. , the extent to which model parameter variations occur and the effect of changing the systems point of operation.
Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions.
Control systems include many components, such as transducers, sensors, actuators and mechanical parts. These components are required to be operated under some specific conditions. However, due to prolonged operations or harsh operating environment, the properties of these devices may degrade to an unacceptable level, causing more regular fault occurrences. It is therefore necessary to diagnose faults and provide the fault-accommodation control which compensates for the fault of the component by substituting a configuration of redundant elements so that the system continues to operate satisfactorily. In this book, we present a result of several years of work in the area of fault diagnosis and fault-accommodation control. It aims at information estimate methods when faults occur. The book uses the model built from the plant or process, to detect and isolate failures, in contrast to traditional hardware or statistical technologies dealing with failures. It presents model-based learning and design technologies for fault detection, isolation and identification as well as fault-tolerant control. These models are also used to analyse the fault detectability and isolability conditions and discuss the stability of the closed-loop system. It is intended to report new technologies in the area of fault diagnosis, covering fault analysis and control strategies of design for various applications. The book addresses four main schemes: modelling of actuator or sensor faults; fault detection and isolation; fault identification, and fault reconfiguration (accommodation) control. It also covers application issues in the monitoring control of actuators, providing several interesting case studies for more application-oriented readers.
The reliability of induction motors is a major requirement in many industrial applications. It is especially important where an unexpected breakdown might result in the interruption of critical services such as military operations, transportation, aviation, and medical applications. Advanced Condition Monitoring and Fault Diagnosis of Electric Machines is a collection of innovative research on various issues related to machinery condition monitoring, signal processing and conditioning, instrumentation and measurements, and new trends in condition monitoring. It also pays special attention to the fault identification process. While highlighting topics including spectral analysis, electrical engineering, and bearing faults, this book is an ideal reference source for electrical engineers, mechanical engineers, researchers, and graduate-level students seeking current research on various methods of maintaining machinery.
Authors are experts in the field and have published books as well as articles in first-rate journals Comprehensive resource that contains many MATLAB-based examples
Featuring a model-based approach to fault detection and diagnosis in engineering systems, this book contains up-to-date, practical information on preventing product deterioration, performance degradation and major machinery damage.;College or university bookstores may order five or more copies at a special student price. Price is available upon request.
This book considers two popular topics: fault detection and isolation (FDI) and flight data estimation using flush air data sensing (FADS) systems. Literature surveys, comparison tests, simulations and wind tunnel tests are performed. In both cases, a UAV platform is considered for demonstration purposes. In the first part of the book, FDI is considered for sensor faults where a neural network approach is implemented. FDI is applied both in academia and industry resulting in many publications over the past 50 years or so. However few publications consider neural networks in comparison to traditional techniques such as observer based, parameter estimations and parity space approaches. The second part of this book focuses on how to estimate flight data (angle of attack, airspeed) using a matrix of pressure sensors and a neural network model. In conclusion this book can serve as an introduction to FDI and FADS systems, a literature survey, and a case study for UAV applications.

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