Download or read book Online Fault Detection and Isolation of Nonlinear Systems Based on Neurofuzzy Networks written by Hing-Tung Mok and published by Open Dissertation Press. This book was released on 2017-01-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Online Fault Detection and Isolation of Nonlinear Systems Based on Neurofuzzy Networks" by Hing-tung, Mok, 莫興東, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. DOI: 10.5353/th_b3979406 Subjects: Fault location (Engineering) Neural networks (Computer science) Fuzzy systems

Download or read book Online Fault Detection and Isolation of Nonlinear Systems Based on Neurofuzzy Networks written by Hing-tung Mok and published by . This book was released on 2008 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Neural Network Based State Estimation of Nonlinear Systems written by Heidar A. Talebi and published by Springer. This book was released on 2009-12-04 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.

Download or read book Fault Detection Supervision and Safety of Technical Processes 2006 written by Hong-Yue Zhang and published by Elsevier. This book was released on 2007-03-01 with total page 1576 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Neural Network Based State Estimation of Nonlinear Systems written by Heidar A. Talebi and published by Springer. This book was released on 2009-12-14 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.

Download or read book Monitoring and Control of Information Poor Systems written by Arthur L. Dexter and published by John Wiley & Sons. This book was released on 2012-04-09 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: The monitoring and control of a system whose behaviour is highly uncertain is an important and challenging practical problem. Methods of solution based on fuzzy techniques have generated considerable interest, but very little of the existing literature considers explicit ways of taking uncertainties into account. This book describes an approach to the monitoring and control of information-poor systems that is based on fuzzy relational models which generate fuzzy outputs. The first part of Monitoring and Control of Information-Poor Systems aims to clarify why design decisions must take account of the uncertainty associated with optimal choices, and to explain how a fuzzy relational model can be used to generate a fuzzy output, which reflects the uncertainties associated with its predictions. Part two gives a brief introduction to fuzzy decision-making and shows how it can be used to design a predictive control scheme that is suitable for controlling information-poor systems using inaccurate measurements. Part three describes different ways in which fuzzy relational models can be generated online and explains the practical issues associated with their identification and application. The final part of the book provides examples of the use of the previously described techniques in real applications. Key features: Describes techniques applicable to a wide range of engineering, environmental, medical, financial and economic applications Uses simple examples to help explain the basic techniques for dealing with uncertainty Describes a novel design approach based on the use of fuzzy relational models Considers practical issues associated with applying the techniques to real systems Monitoring and Control of Information-Poor Systems forms an invaluable resource for a wide range of graduate students, and is also a comprehensive reference for researchers and practitioners working on problems involving mathematical modelling and control.

Download or read book Proceedings of the 2015 International Conference on Electrical and Information Technologies for Rail Transportation written by Limin Jia and published by Springer. This book was released on 2016-03-11 with total page 853 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings collect the latest research trends, methods and experimental results in the field of electrical and information technologies for rail transportation. The topics cover intelligent computing, information processing, communication technology, automatic control, and their applications in rail transportation etc. The proceedings can be a valuable reference work for researchers and graduate students working in rail transportation, electrical engineering and information technologies.

Download or read book Diagnosis and Fault tolerant Control Volume 2 written by Vicenc Puig and published by John Wiley & Sons. This book was released on 2021-11-30 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances in fault diagnosis and fault-tolerant control of dynamic processes. Its impetus derives from the need for an overview of the challenges of the fault diagnosis technique and sustainable control, especially for those demanding systems that require reliability, availability, maintainability, and safety to ensure efficient operations. Moreover, the need for a high degree of tolerance with respect to possible faults represents a further key point, primarily for complex systems, as modeling and control are inherently challenging, and maintenance is both expensive and safety-critical. Diagnosis and Fault-tolerant Control 2 also presents and compares different fault diagnosis and fault-tolerant schemes, using well established, innovative strategies for modeling the behavior of the dynamic process under investigation. An updated treatise of diagnosis and fault-tolerant control is addressed with the use of essential and advanced methods including signal-based, model-based and data-driven techniques. Another key feature is the application of these methods for dealing with robustness and reliability.

Download or read book Fault Detection Isolation and Identification for Nonlinear Systems Using a Hybrid Approach written by Ehsan Sobahni-Tehrani and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Model Based Fault Detection and Isolation in Nonlinear Dynamic Systems written by Vasanth Krishnaswami and published by . This book was released on 1996 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust fault detection and isolation of nonlinear systems with augmented state models written by Jochen Aßfalg and published by . This book was released on 2009 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Fault Diagnosis in Nonlinear Systems Using Learning and Sliding Mode Approaches with Applications for Satellite Control Systems written by Qing Wu and published by . This book was released on 2008 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, model based fault detection, isolation, and estimation problem in several classes of nonlinear systems is studied using sliding mode and learning approaches. First, a fault diagnosis scheme using a bank of repetitive learning observers is presented. The diagnostic observers are established in a generalized observer scheme, and the observer inputs are repetitively updated using the output estimation error in a proportional-integral structure. Next, a framework for robust fault diagnosis using sliding mode and learning approaches is proposed to deal with various types of faults in a class of nonlinear systems with triangular input form. In the designed diagnostic observers, first order and second order sliding modes are used respectively, to achieve robust state estimation in the presence of uncertainties, and additional online estimators are established to characterize the faults. In order to guarantee that the sliding mode is able to distinguish the system uncertainties from the faults, two iterative adaptive laws are used to update the sliding mode switching gains. Moreover, different online fault estimators are developed using neural state space models, iterative learning algorithms, and wavelet networks. Another class of nonlinear systems where an unmeasurable part of state can be described as a nonlinear function of the output and its derivatives is considered next. Accordingly, a class of fault diagnosis schemes using high order sliding mode differentiators (HOSMDs) and online estimators are proposed, where neural adaptive estimators and iterative neuron PID estimators are designed. Additionally, a fault diagnosis scheme using HOSMDs and neural networks based uncertainty observers is designed in order to achieve a better performance in robust fault detection. If the uncertainties can be accurately estimated, the generated diagnostic residual is more sensitive to the onset of faults. Finally, a fault diagnosis scheme using Takagi-Sugeno (TS) fuzzy models, neural networks, and sliding mode is developed. The availability of TS fuzzy models makes this fault diagnosis scheme applicable to a wider class of nonlinear systems. The proposed fault diagnosis schemes are applied to several types of satellite control systems, and the simulation results demonstrate their performance.

Download or read book Observer based Fault Detection and Isolation for Nonlinear Systems written by T. F. Lootsma and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Predictive Maintenance in Dynamic Systems written by Edwin Lughofer and published by Springer. This book was released on 2019-02-28 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power.

Download or read book Data based Fault Detection and Isolation for Nonlinear Process Systems Using Feedback Control written by Benjamin J. Ohran and published by . This book was released on 2009 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Nonlinear Model based Fault Detection and Isolation written by Iván Castillo and published by . This book was released on 2011 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation addresses fault detection and isolation (FDI) for nonlinear systems based on models using two different approaches. The first approach detects and isolates single and multiple faults, particularly when there are restrictions in measuring process variables. The FDI model-based method is based on nonlinear state estimators, in which the estimates are calculated under high filtering, and a high fidelity residuals model, obtained from the difference between measurements and estimates. In the second approach, a robust fault detection and isolation (RFDI) system, that handles both parameter estimation and parameters with uncertainties, is proposed in which complex models can be simplified with nonlinear functions so that they can be formulated as differential algebraic equations (DAE). In utilizing this framework, faults are identified by performing a statistical analysis. Finally, comparisons with existing data-driven approaches show that the proposed model-based methods are capable of distinguishing a fault from the diverse array of possible faults, a common occurrence in complex processes.

Download or read book Fault Detection and Isolation Based on Adaptive Observers for Nonlinear Dynamic Systems written by Qinghua Zhang and published by . This book was released on 1999 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: