Download or read book Fault Diagnosis and Prognosis Techniques for Complex Engineering Systems written by Hamid Reza Karimi and published by Academic Press. This book was released on 2021-06-05 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fault Diagnosis and Prognosis Techniques for Complex Engineering Systems gives a systematic description of the many facets of envisaging, designing, implementing, and experimentally exploring emerging trends in fault diagnosis and failure prognosis in mechanical, electrical, hydraulic and biomedical systems. The book is devoted to the development of mathematical methodologies for fault diagnosis and isolation, fault tolerant control, and failure prognosis problems of engineering systems. Sections present new techniques in reliability modeling, reliability analysis, reliability design, fault and failure detection, signal processing, and fault tolerant control of engineering systems. Sections focus on the development of mathematical methodologies for diagnosis and prognosis of faults or failures, providing a unified platform for understanding and applicability of advanced diagnosis and prognosis methodologies for improving reliability purposes in both theory and practice, such as vehicles, manufacturing systems, circuits, flights, biomedical systems. This book will be a valuable resource for different groups of readers – mechanical engineers working on vehicle systems, electrical engineers working on rotary machinery systems, control engineers working on fault detection systems, mathematicians and physician working on complex dynamics, and many more. - Presents recent advances of theory, technological aspects, and applications of advanced diagnosis and prognosis methodologies in engineering applications - Provides a series of the latest results, including fault detection, isolation, fault tolerant control, failure prognosis of components, and more - Gives numerical and simulation results in each chapter to reflect engineering practices
Download or read book Diagnostics and Prognostics of Engineering Systems Methods and Techniques written by Kadry, Seifedine and published by IGI Global. This book was released on 2012-09-30 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: Industrial Prognostics predicts an industrial systems lifespan using probability measurements to determine the way a machine operates. Prognostics are essential in determining being able to predict and stop failures before they occur. Therefore the development of dependable prognostic procedures for engineering systems is important to increase the systems performance and reliability. Diagnostics and Prognostics of Engineering Systems: Methods and Techniques provides widespread coverage and discussions on the methods and techniques of diagnosis and prognosis systems. Including practical examples to display the methods effectiveness in real-world applications as well as the latest trends and research, this reference source aims to introduce fundamental theory and practice for system diagnosis and prognosis.
Download or read book Intelligent Fault Diagnosis and Prognosis for Engineering Systems written by George Vachtsevanos and published by Wiley. This book was released on 2006-09-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expert guidance on theory and practice in condition-based intelligent machine fault diagnosis and failure prognosis Intelligent Fault Diagnosis and Prognosis for Engineering Systems gives a complete presentation of basic essentials of fault diagnosis and failure prognosis, and takes a look at the cutting-edge discipline of intelligent fault diagnosis and failure prognosis technologies for condition-based maintenance. It thoroughly details the interdisciplinary methods required to understand the physics of failure mechanisms in materials, structures, and rotating equipment, and also presents strategies to detect faults or incipient failures and predict the remaining useful life of failing components. Case studies are used throughout the book to illustrate enabling technologies. Intelligent Fault Diagnosis and Prognosis for Engineering Systems offers material in a holistic and integrated approach that addresses the various interdisciplinary components of the field--from electrical, mechanical, industrial, and computer engineering to business management. This invaluably helpful book: * Includes state-of-the-art algorithms, methodologies, and contributions from leading experts, including cost-benefit analysis tools and performance assessment techniques * Covers theory and practice in a way that is rooted in industry research and experience * Presents the only systematic, holistic approach to a strongly interdisciplinary topic
Download or read book Fault Detection Diagnosis and Prognosis written by Fausto Pedro García Márquez and published by BoD – Books on Demand. This book was released on 2020-02-05 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the main concepts, state of the art, advances, and case studies of fault detection, diagnosis, and prognosis. This topic is a critical variable in industry to reach and maintain competitiveness. Therefore, proper management of the corrective, predictive, and preventive politics in any industry is required. This book complements other subdisciplines such as economics, finance, marketing, decision and risk analysis, engineering, etc. The book presents real case studies in multiple disciplines. It considers the main topics using prognostic and subdiscipline techniques. It is essential to link these topics with the areas of finance, scheduling, resources, downtime, etc. to increase productivity, profitability, maintainability, reliability, safety, and availability, and reduce costs and downtime. Advances in mathematics, modeling, computational techniques, dynamic analysis, etc. are employed analytically. Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support the analysis of prognostic problems with defined constraints and requirements. The book is intended for graduate students and professionals in industrial engineering, business administration, industrial organization, operations management, applied microeconomics, and the decisions sciences, either studying maintenance or needing to solve large, specific, and complex maintenance management problems as part of their jobs. The work will also be of interest to researches from academia.
Download or read book Fault Detection and Diagnosis in Engineering Systems written by Janos Gertler and published by Routledge. This book was released on 2017-11-22 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems written by Yongbo Li and published by Mdpi AG. This book was released on 2023-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries.
Download or read book Special Issue On Fault Diagnosis and Prognosis for Engineering Systems written by Zhihong Man and published by . This book was released on 2014 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Big Data Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems written by Yaguo Lei and published by Springer Nature. This book was released on 2022-10-19 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM). Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era. Features: Addresses the critical challenges in the field of PHM at present Presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis Provides abundant experimental validations and engineering cases of the presented methodologies
Download or read book Intelligent Fault Diagnosis and Health Assessment for Complex Electro Mechanical Systems written by Weihua Li and published by Springer Nature. This book was released on 2023-09-10 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on AI and machine learning, this book systematically presents the theories and methods for complex electro-mechanical system fault prognosis, intelligent diagnosis, and health state assessment in modern industry. The book emphasizes feature extraction, incipient fault prediction, fault classification, and degradation assessment, which are based on supervised-, semi-supervised-, manifold-, and deep learning; machinery degradation state tracking and prognosis by phase space reconstruction; and complex electro-mechanical system reliability assessment and health maintenance based on running state info. These theories and methods are integrated with practical industrial applications, which can help the readers get into the field more smoothly and provide an important reference for their study, research, and engineering practice.
Download or read book Fault Diagnosis Systems written by Rolf Isermann and published by Springer Science & Business Media. This book was released on 2006-01-16 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Fault Diagnosis of Dynamic Systems written by Teresa Escobet and published by Springer. This book was released on 2019-06-22 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fault Diagnosis of Dynamic Systems provides readers with a glimpse into the fundamental issues and techniques of fault diagnosis used by Automatic Control (FDI) and Artificial Intelligence (DX) research communities. The book reviews the standard techniques and approaches widely used in both communities. It also contains benchmark examples and case studies that demonstrate how the same problem can be solved using the presented approaches. The book also introduces advanced fault diagnosis approaches that are currently still being researched, including methods for non-linear, hybrid, discrete-event and software/business systems, as well as, an introduction to prognosis. Fault Diagnosis of Dynamic Systems is valuable source of information for researchers and engineers starting to work on fault diagnosis and willing to have a reference guide on the main concepts and standard approaches on fault diagnosis. Readers with experience on one of the two main communities will also find it useful to learn the fundamental concepts of the other community and the synergies between them. The book is also open to researchers or academics who are already familiar with the standard approaches, since they will find a collection of advanced approaches with more specific and advanced topics or with application to different domains. Finally, engineers and researchers looking for transferable fault diagnosis methods will also find useful insights in the book.
Download or read book Advanced methods for fault diagnosis and fault tolerant control written by Steven X. Ding and published by Springer Nature. This book was released on 2020-11-24 with total page 664 pages. Available in PDF, EPUB and Kindle. Book excerpt: The major objective of this book is to introduce advanced design and (online) optimization methods for fault diagnosis and fault-tolerant control from different aspects. Under the aspect of system types, fault diagnosis and fault-tolerant issues are dealt with for linear time-invariant and time-varying systems as well as for nonlinear and distributed (including networked) systems. From the methodological point of view, both model-based and data-driven schemes are investigated.To allow for a self-contained study and enable an easy implementation in real applications, the necessary knowledge as well as tools in mathematics and control theory are included in this book. The main results with the fault diagnosis and fault-tolerant schemes are presented in form of algorithms and demonstrated by means of benchmark case studies. The intended audience of this book are process and control engineers, engineering students and researchers with control engineering background.
Download or read book Fault Detection and Diagnosis in Industrial Systems written by L.H. Chiang and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: Early and accurate fault detection and diagnosis for modern chemical plants can minimize downtime, increase the safety of plant operations, and reduce manufacturing costs. This book presents the theoretical background and practical techniques for data-driven process monitoring. It demonstrates the application of all the data-driven process monitoring techniques to the Tennessee Eastman plant simulator, and looks at the strengths and weaknesses of each approach in detail. A plant simulator and problems allow readers to apply process monitoring techniques.
Download or read book Fault Diagnosis and Prognosis System for Aircraft written by Zefeng Wang and published by . This book was released on 2013 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this thesis is to build an effective and practical intelligent system to diagnose and prognose aircraft faults. My research focuses on “The MOdeling, DIagnosis and PROgnosis (MODIPRO)” faults in complex systems. This work is a part of a project entitled FUI MODIPRO which is supported by Dassault Aviation. The objective of this project is to research and develop a software solution MODIPRO Version 0 and put it on the aviation market. This software solution can analyze a huge mass of data acquired from a flight and a fleet of aircraft, and the system can deduce rules for diagnosis and prognosis of faults. The system proposed in this thesis has been fully tested by using actual experimental data from a tri-engines system of aircrafts Z1, Z2 and Z3 (supplied by Dassault Aviation). The whole system would be built on a database containing about 67 hours of flight records involving 32 sensors. With the rapid development of modern aero technology and the market demand of high- performance, aircraft systems have become more and more. Thus, the classical diagnosis methods become less available. In the state of the art, unplanned maintenance takes place only at breakdowns, which is too late to observe the faults; the planned maintenance costs too much financial resources and manpower, which needs to set a periodic interval to perform preventive maintenance regardless of the health status of a physical asset. Although Build in Test (BIT) system is used widely, it also costs too much human and financial resource. In a general way the maintenance staffs need to connect the diagnostic box to the aircraft via interface after each flight mission. Because these classical methods often cause the false alarm, the planned maintenance is also indispensable today. In addition, classical diagnostic and prognostic system, such as Condition-Based Maintenance (CBM) and Prognostic Health Management (PHM), analyze the health state of aircrafts when they are on the ground - in the "offline" mode, they can't supervise the aircraft during the mission. In order to resolve these problems and guarantee a high ratio of attendance of aircraft, the system proposed in this thesis uses machine-learning methods to automatically detect, isolate, and even forecast aircraft faults while maintaining reliability and safety. The researches involve signals processing techniques, pattern recognition and classification. On the one hand, the diagnostic model allows the system to deduce the "real" cause of a fault by the observation and the treatment of acquired signals from flight records. On the other hand, the model can provide a progress of degradation of the health state and thus allows anticipating the faults or deferring the needless planned maintenance. The diagnosis system can locate and identify faults and the prognosis system can make the arbitration of a future maintenance plan on basis of the operating needs, the costs of rehabilitation, the risk of fault and the consequences. In addition to this, the system proposed in this thesis can be used not only in the off-line mode when aircraft maintenance occurs, but also in the on-line mode during the aircraft's mission. According to the different situations requirements, the missions of on-line system and off-line system are different. The on-line system is tasked with detecting faults and sending the alarms to the pilot and the Aircraft Ground Center (AGC) in time. The off-line system is obliged to locate the fault(s) and make a detail report to the maintenance center. Additionally, the system needs to analyze the flight data in the past time for the sake of forecasting the fault(s). In order to ensure the reliability of the system, different methods of machine learning are used in parallel as subsystems. These methods can compensate the disadvantages of each other. At first, the data are analyzed and pre-classified by Linear Analysis Discriminant (LDA), a classical and simple approach. On basis of the results, a novel approach of classification called SCM is proposed to improve the accuracy of diagnosis. SCM is different from SVM that requires the support vectors on the boundary of every class to distinguish the categories. SCM seeks the support vectors of true centers and sub-centers of each class during the machine learning. It can make the corresponding centers as the model of the class. The classification of data is simply done by the power distances of the centers. Furthermore, SCM can work for the prognosis analysis and perfectly deal with the nonlinear problem. The evolution of flight data is supervised by each fault model. On the basis of the evolution of the distances from the cloud of data to the centers, the system estimates the tendency of the evolution of data and forecast the probable faults in the future. Beyond a short-term prognosis of faults, the system can also be used to do a long-term evaluation of aircraft healthy state. This is more convincing and efficacious compared to regression methods and statistical methods, which lack the precision of a long-term regression and which require a longer time for data analysis. Although the diagnosis results of SCM and SVM are already satisfied with a correct detection rate that exceeds 95%, Artificial Neural Networks (ANN) are used to build another sub-system, so as to analyze the impact of using different types sensors on the different fault diagnosis and confirm the results from the models SVM and SCM. ANN is a quite different AI technic from SCM and SVM. It is a mathematical model that is inspired by the structure and functional aspects of biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. All the sensors are divided in to different groups corresponding to different types of the sensors. Different combinations of sensors are linked to the neural networks, thus we can study the importance of different types of aircraft sensors by the weights of networks and the diagnosis results of the faults. The methods, as SCM, SVM and ANN, need much time to accomplish machine learning, which cannot do the learning during the flight mission. But, in some cases, it may be necessary to rebuild the diagnosis system, for example if some sensors are broken or lost during the mission. For overcoming this, we added sub-systems based on decision trees (DT) and Gaussian mixture models (GMM), which are easier to interpret, quicker to learn than other data-driven methods, and able to work even with missing pieces of information. The C4.5 algorithm automatically "learns" the best decision tree by performing a search through the set of possible trees according to the available training data. Its needs less time to accomplish the machine learning, so it is also studied and improved in this thesis, and be used to build a subsystem for sake of restructuring the diagnosis system if some sensors or sensors information are lost, especially under the condition of war. GMM can also draw the plan of dysfunctional models and monitor the evolution of the health state of the aircraft in the prognosis system. Unlike expert systems or other conventional methods, the methods developed in this thesis can easily integrate new faults and new rules in the database: there isn't any conflict between the new and old rules. Beyond that, there is another important problem to consider and resolve: some sensors might be already failed before the machine learning. The measurements via sensors in the aircraft are used as the inputs of the system. The nature of the sensors will impact the accuracy and confidence of the diagnosis and prognosis results of the system. Thus, these data should be treated above of all. First, the system needs to check the healthy state of the sensors. If some sensors are broken down, the original system is not applicable. The system will start the emergency application, like fast relearning of the decision tree in order to build a new temporary fault diagnosis system. In addition to that, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used in data mining. They can not only reduce the input data's dimension, but also make a visualization of data in 2D or 3D. It is very useful to observe the evaluation of flux data and to realize prognosis, and it is important for engineers to study the nature of faults. The system described here is not a black box. Although the system is built mainly for combat aircraft, it can be applied to all other types of aircraft, namely civil aircraft. On one hand, the system and its dysfunction models of aircraft faults can be designed to illuminate engineering consulting services responsible for monitoring the condition of aircrafts to ensure the safety of clients. On the other hand, this system can also accumulate the knowledge for re-engineering purposes (including diagnosis operational rules) and perfect the design of new aircrafts.
Download or read book Computational Intelligence in Fault Diagnosis written by Vasile Palade and published by Springer Science & Business Media. This book was released on 2006-12-22 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the most recent concerns and research results in industrial fault diagnosis using intelligent techniques. It focuses on computational intelligence applications to fault diagnosis with real-world applications used in different chapters to validate the different diagnosis methods. The book includes one chapter dealing with a novel coherent fault diagnosis distributed methodology for complex systems.
Download or read book Data driven Techniques for Fault Diagnosis and Condition Assessment of Complex Engineering and Socio economic Systems written by Kihoon Choi and published by . This book was released on 2005 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Filter Based Fault Diagnosis and Remaining Useful Life Prediction written by Yong Zhang and published by CRC Press. This book was released on 2023-02-10 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book unifies existing and emerging concepts concerning state estimation, fault detection, fault isolation and fault estimation on industrial systems with an emphasis on a variety of network-induced phenomena, fault diagnosis and remaining useful life for industrial equipment. It covers state estimation/monitor, fault diagnosis and remaining useful life prediction by drawing on the conventional theories of systems science, signal processing and machine learning. Features: Unifies existing and emerging concepts concerning robust filtering and fault diagnosis with an emphasis on a variety of network-induced complexities. Explains theories, techniques, and applications of state estimation as well as fault diagnosis from an engineering-oriented perspective. Provides a series of latest results in robust/stochastic filtering, multidate sample, and time-varying system. Captures diagnosis (fault detection, fault isolation and fault estimation) for time-varying multi-rate systems. Includes simulation examples in each chapter to reflect the engineering practice. This book aims at graduate students, professionals and researchers in control science and application, system analysis, artificial intelligence, and fault diagnosis.
