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The safe and reliable operation of technical systems is of great significance for the protection of human life and health, the environment, and of the vested economic value. The correct functioning of those systems has a profound impact also on production cost and product quality. The early detection of faults is critical in avoiding performance degradation and damage to the machinery or human life. Accurate diagnosis then helps to make the right decisions on emergency actions and repairs. Fault detection and diagnosis (FDD) has developed into a major area of research, at the intersection of systems and control engineering, artificial intelligence, applied mathematics and statistics, and such application fields as chemical, electrical, mechanical and aerospace engineering. IFAC has recognized the significance of FDD by launching a triennial symposium series dedicated to the subject. The SAFEPROCESS Symposium is organized every three years since the first symposium held in Baden-Baden in 1991. SAFEPROCESS 2006, the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes was held in Beijing, PR China. The program included three plenary papers, two semi-plenary papers, two industrial talks by internationally recognized experts and 258 regular papers, which have been selected out of a total of 387 regular and invited papers submitted. * Discusses the developments and future challenges in all aspects of fault diagnosis and fault tolerant control * 8 invited and 36 contributed sessions included with a special session on the demonstration of process monitoring and diagnostic software tools
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.
This book describes the challenges that critical infrastructure systems face, and presents state of the art solutions to address them. How can we design intelligent systems or intelligent agents that can make appropriate real-time decisions in the management of such large-scale, complex systems? What are the primary challenges for critical infrastructure systems? The book also provides readers with the relevant information to recognize how important infrastructures are, and their role in connection with a society’s economy, security and prosperity. It goes on to describe state-of-the-art solutions to address these points, including new methodologies and instrumentation tools (e.g. embedded software and intelligent algorithms) for transforming and optimizing target infrastructures. The book is the most comprehensive resource to date for professionals in both the private and public sectors, while also offering an essential guide for students and researchers in the areas of modeling and analysis of critical infrastructure systems, monitoring, control, risk/impact evaluation, fault diagnosis, fault-tolerant control, and infrastructure dependencies/interdependencies. The importance of the research presented in the book is reflected in the fact that currently, for the first time in human history, more people live in cities than in rural areas, and that, by 2050, roughly 70% of the world’s total population is expected to live in cities.
This work considers the problem of identifying the fault in a faulty dynamical system on the basis of the system's input and output signals only. For this purpose, a model-based method for the design of diagnostic tests which consist of specific input signals and appropriate residual generators is developed. The method extends the structure graph of dynamical systems in order to represent the couplings in a system which has been brought to a specific operating region. The resulting local structure graph is used to determine specific residual generators which can distinguish between faults on the basis of the system's input and output signals in the corresponding operating region. Algorithms to determine advantageous operating regions and input signals which drive the system into such operating regions are given. The application of the method to determine diagnostic tests is demonstrated using a typical automotive system, a throttle valve.
This volume constitutes the thoroughly refereed post-conference proceedings of the 5th International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2014, held in Bhubaneswar, India, in December 2014. The total of 96 papers presented in this volume was carefully reviewed and selected from 250 submissions for inclusion in the proceedings. The papers cover a wide range of topics in swarm, evolutionary, memetic and other intelligent computing algorithms and their real world applications in problems selected from diverse domains of science and engineering.
There was a time when AI was seen by many as science fiction, i.e., the healthy endeavour of speculating about the future. Now the future is here. AI has passed from being a visionary discipline to lying at the core of many commercial enterprises. AI programs scattered through the web influence nowadays our lives: by extracting profiles and offering tailored advertisement, helping us in our searches, establishing social networks, providing entertainment...And not just in the net, but also in the physical world. In Japan there are robots that guide customers through marketplaces advising them where to find the product matching their needs, and realistic replicas of university professors allow them to teach their lectures a hundred kilometres away from the classroom. Not to speak about intelligent prostheses and remote high-precision surgery. In the Catalan-speaking world there are no robots in marketplaces yet, but it is coming. Recently, the first commercial humanoid robot was built. Since AI technology is becoming reasonably mature, companies are progressively relying on it. The Catalan Association for Artificial Intelligence (ACIA) tries to promote synergies within the research community and also between the different actors playing a role in the development of AI: from universities to industry, from governmental departments to the information society, from entertainment enterprises to citizen services.
This book presents selected fault diagnosis and fault-tolerant control strategies for non-linear systems in a unified framework. In particular, starting from advanced state estimation strategies up to modern soft computing, the discrete-time description of the system is employed Part I of the book presents original research results regarding state estimation and neural networks for robust fault diagnosis. Part II is devoted to the presentation of integrated fault diagnosis and fault-tolerant systems. It starts with a general fault-tolerant control framework, which is then extended by introducing robustness with respect to various uncertainties. Finally, it is shown how to implement the proposed framework for fuzzy systems described by the well-known Takagi–Sugeno models. This research monograph is intended for researchers, engineers, and advanced postgraduate students in control and electrical engineering, computer science, as well as mechanical and chemical engineering.
This book gives a wide-ranging description of the many facets of complex dynamic networks and systems within an infrastructure provided by integrated control and supervision: envisioning, design, experimental exploration, and implementation. The theoretical contributions and the case studies presented can reach control goals beyond those of stabilization and output regulation or even of adaptive control. Reporting on work of the Control of Complex Systems (COSY) research program, Complex Systems follows from and expands upon an earlier collection: Control of Complex Systems by introducing novel theoretical techniques for hard-to-control networks and systems. The major common feature of all the superficially diverse contributions encompassed by this book is that of spotting and exploiting possible areas of mutual reinforcement between control, computing and communications. These help readers to achieve not only robust stable plant system operation but also properties such as collective adaptivity, integrity and survivability at the same time retaining desired performance quality. Applications in the individual chapters are drawn from: • the general implementation of model-based diagnosis and systems engineering in medical technology, in communication, and in power and airport networks; • the creation of biologically inspired control brains and safety-critical human–machine systems, • process-industrial uses; • biped robots; • large space structures and unmanned aerial vehicles; and • precision servomechanisms and other advanced technologies. Complex Systems provides researchers from engineering, applied mathematics and computer science backgrounds with innovative theoretical and practical insights into the state-of-the-art of complex networks and systems research. It employs physical implementations and extensive computer simulations. Graduate students specializing in complex-systems research will also learn much from this collection./pp
This work presents a novel approach to modeling, analysis and diagnosis of coupled mechatronical systems with partially autonomous behavior and asynchronous state transitions. The systems under consideration are assumed to have the following properties: The internal interactions are immeasurable but reliable and the measurements relevant for diagnosis are given as a sequence of events. Asynchronous networks of input/output automata (I/O-automata) are developed to cope with partial coupling between components and to reduce the computational complexity of the diagnostic algorithms. I/O-automata are used to model those components. Their measurable inputs and outputs are modeled as control signals. Interconnection signals are used to model the internal dependencies among the components. They are linked via an interaction block to one another. The criterion known from synchronous networks of I/O-automata is extended to ensure the well-posedness of this modeling formalism. To check for partially autonomous behavior, two types of autonomy are introduced and discussed: Structural autonomy and state-dependent autonomy. To carry out the diagnosis, three different information structures are investigated: Centralized, decentralized and partially coordinated. The centralized approach yields the ideal diagnostic result, but reduction of the computational complexity by using online composition is rather small. Further reduction of the computational complexity is accomplished by decentralized diagnosis. It yields only in the case of state-dependent autonomy a complete and sound diagnostic result. In general, the lack of soundness arises. Both, obtaining an ideal diagnostic result and reducing the computational complexity, is obtained by the partially coordinated diagnostic algorithm.
This book is a collection of 45 accepted papers originally submitted for the 12th International Conference of the Catalan Association for Artificial Intelligence (ACIA). It also includes a brief summary of two papers from invited speakers. The Catalan Association for Artificial Intelligence was founded in 1994 with the aim of fostering cooperation among researchers from the Catalan-speaking AI research community. Collaboration between ACIA members and the wider international AI community has also been wel-established now for many years. The papers in these proceedings reflect this collaboration and include contributions not only from the Catalan-speaking regions of Spain, but also from France and Italy, and from as far afield as Mexico and Australia. Of al the fields in computer science, AI is the one most intertwined with all sorts of disciplines dealt with in the human experience, often employing lessons learnt in one discipline to implement a task in another. The papers in this volume reflect the rich iversity in AI, covering areas such as logics, natural language, machine learning, computer vision, robotics and multi-agent systems.