Data Driven and Model Based Methods for Fault Detection and Diagnosis

Data Driven and Model Based Methods for Fault Detection and Diagnosis
Author: Majdi Mansouri,Mohamed-Faouzi Harkat,Hazem Nounou,Mohamed N. Nounou
Publsiher: Elsevier
Total Pages: 322
Release: 2020-02-05
ISBN: 0128191651
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Data Driven and Model Based Methods for Fault Detection and Diagnosis Book Excerpt:

Data-Driven and Model-Based Methods for Fault Detection and Diagnosis covers techniques that improve the quality of fault detection and enhance monitoring through chemical and environmental processes. The book provides both the theoretical framework and technical solutions. It starts with a review of relevant literature, proceeds with a detailed description of developed methodologies, and then discusses the results of developed methodologies, and ends with major conclusions reached from the analysis of simulation and experimental studies. The book is an indispensable resource for researchers in academia and industry and practitioners working in chemical and environmental engineering to do their work safely. Outlines latent variable based hypothesis testing fault detection techniques to enhance monitoring processes represented by linear or nonlinear input-space models (such as PCA) or input-output models (such as PLS) Explains multiscale latent variable based hypothesis testing fault detection techniques using multiscale representation to help deal with uncertainty in the data and minimize its effect on fault detection Includes interval PCA (IPCA) and interval PLS (IPLS) fault detection methods to enhance the quality of fault detection Provides model-based detection techniques for the improvement of monitoring processes using state estimation-based fault detection approaches Demonstrates the effectiveness of the proposed strategies by conducting simulation and experimental studies on synthetic data

Fault Detection and Diagnosis in Industrial Systems

Fault Detection and Diagnosis in Industrial Systems
Author: L.H. Chiang,E.L. Russell,R.D. Braatz
Publsiher: Springer Science & Business Media
Total Pages: 279
Release: 2000-12-11
ISBN: 9781852333270
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Fault Detection and Diagnosis in Industrial Systems 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.

Diagnosis and Fault tolerant Control 1

Diagnosis and Fault tolerant Control 1
Author: Vicenc Puig,Silvio Simani
Publsiher: John Wiley & Sons
Total Pages: 288
Release: 2021-12-01
ISBN: 1119882311
Category: Computers
Language: EN, FR, DE, ES & NL

Diagnosis and Fault tolerant Control 1 Book Excerpt:

This book presents recent advances in fault diagnosis strategies for complex dynamic systems. Its impetus derives from the need for an overview of the challenges of the fault diagnosis technique, 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 1 also presents and compares different diagnosis schemes using established case studies that are widely used in related literature. The main features of this book regard the analysis, design and implementation of proper solutions for the problems of fault diagnosis in safety critical systems. The design of the considered solutions involves robust data-driven, model-based approaches.

Data Driven and Model Based Methods for Fault Detection and Diagnosis

Data Driven and Model Based Methods for Fault Detection and Diagnosis
Author: Majdi Mansouri,Mohamed-Faouzi Harkat,Hazem N Nounou,Mohamed N Nounou
Publsiher: Elsevier
Total Pages: 412
Release: 2020-02-28
ISBN: 9780128191644
Category: Electronic Book
Language: EN, FR, DE, ES & NL

Data Driven and Model Based Methods for Fault Detection and Diagnosis Book Excerpt:

The main objective of Data-Driven and Model-Based Methods for Fault Detection and Diagnosis is to develop techniques that improve the quality of fault detection and then utilize these developed techniques to enhance monitoring various chemical and environmental processes. The book provides both the theoretical framework and technical solutions. It starts with reviewing relevant literature, proceeds with a detailed description of developed methodologies, followed by a discussion of the results of developed methodologies, and ends with major conclusions reached from the analysis of simulation and experimental studies. The book is an indispensable resource for researchers in academia and industry and practitioners working in chemical and environmental engineering to do their work safely. Outlines latent variable based hypothesis testing fault detection techniques to enhance monitoring processes represented by linear or nonlinear input-space models (such as PCA) or input-output models (such as PLS) Explains multiscale latent variable based hypothesis testing fault detection techniques using multiscale representation to help deal with uncertainty in the data and minimize its effect on fault detection Includes interval PCA (IPCA) and interval PLS (IPLS) fault detection methods to enhance the quality of fault detection Provides model-based detection techniques for improvement of monitoring processes using state estimation-based fault detection approaches Demonstrates the effectiveness of the proposed strategies by conducting simulation and experimental studies on synthetic data

Advanced methods for fault diagnosis and fault tolerant control

Advanced methods for fault diagnosis and fault tolerant control
Author: Steven X. Ding
Publsiher: Springer Nature
Total Pages: 658
Release: 2020-11-24
ISBN: 3662620049
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Advanced methods for fault diagnosis and fault tolerant control 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.

Data driven Design of Fault Diagnosis and Fault tolerant Control Systems

Data driven Design of Fault Diagnosis and Fault tolerant Control Systems
Author: Steven X. Ding
Publsiher: Springer Science & Business Media
Total Pages: 300
Release: 2014-04-12
ISBN: 1447164105
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Data driven Design of Fault Diagnosis and Fault tolerant Control Systems Book Excerpt:

Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis, and control methods and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering to the needs of dynamic industrial processes. With ever increasing demands for reliability, availability and safety in technical processes and assets, process monitoring and fault-tolerance have become important issues surrounding the design of automatic control systems. This text shows the reader how, thanks to the rapid development of information technology, key techniques of data-driven and statistical process monitoring and control can now become widely used in industrial practice to address these issues. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book. Major schemes are presented in algorithm form and demonstrated on industrial case systems. Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems will be of interest to process and control engineers, engineering students and researchers with a control engineering background.

Data driven Detection and Diagnosis of Faults in Traction Systems of High speed Trains

Data driven Detection and Diagnosis of Faults in Traction Systems of High speed Trains
Author: Hongtian Chen,Bin Jiang,Ningyun Lu,Wen Chen
Publsiher: Springer Nature
Total Pages: 160
Release: 2020-04-25
ISBN: 3030462633
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Data driven Detection and Diagnosis of Faults in Traction Systems of High speed Trains Book Excerpt:

This book addresses the needs of researchers and practitioners in the field of high-speed trains, especially those whose work involves safety and reliability issues in traction systems. It will appeal to researchers and graduate students at institutions of higher learning, research labs, and in the industrial R&D sector, catering to a readership from a broad range of disciplines including intelligent transportation, electrical engineering, mechanical engineering, chemical engineering, the biological sciences and engineering, economics, ecology, and the mathematical sciences.

Data Driven Fault Detection for Industrial Processes

Data Driven Fault Detection for Industrial Processes
Author: Zhiwen Chen
Publsiher: Springer
Total Pages: 112
Release: 2017-01-02
ISBN: 3658167564
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Data Driven Fault Detection for Industrial Processes Book Excerpt:

Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed.

Model and Data driven Approaches to Fault Detection and Isolation in Complex Systems

Model  and Data driven Approaches to Fault Detection and Isolation in Complex Systems
Author: Hamed Khorasgani
Publsiher: Unknown
Total Pages: 135
Release: 2018
ISBN: 1928374650XXX
Category: Electronic dissertations
Language: EN, FR, DE, ES & NL

Model and Data driven Approaches to Fault Detection and Isolation in Complex Systems Book Excerpt:

Algorithms for Fault Detection and Diagnosis

Algorithms for Fault Detection and Diagnosis
Author: Francesco Ferracuti,Alessandro Freddi,Andrea Monteriù
Publsiher: MDPI
Total Pages: 130
Release: 2021-03-19
ISBN: 3036504621
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Algorithms for Fault Detection and Diagnosis Book Excerpt:

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.

Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches

Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches
Author: Fouzi Harrou,Ying Sun,Amanda S. Hering,Muddu Madakyaru,abdelkader Dairi
Publsiher: Elsevier
Total Pages: 328
Release: 2020-07-03
ISBN: 0128193662
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches Book Excerpt:

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches – such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches – to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems. Uses a data-driven based approach to fault detection and attribution Provides an in-depth understanding of fault detection and attribution in complex and multivariate systems Familiarises you with the most suitable data-driven based techniques including multivariate statistical techniques and deep learning-based methods Includes case studies and comparison of different methods

Engineering Applications of Neural Networks

Engineering Applications of Neural Networks
Author: Lazaros Iliadis,Chrisina Jayne,Anastasios Tefas,Elias Pimenidis
Publsiher: Springer Nature
Total Pages: 530
Release: 2022-07-16
ISBN: 3031082230
Category: Computers
Language: EN, FR, DE, ES & NL

Engineering Applications of Neural Networks Book Excerpt:

This book constitutes the refereed proceedings of the 23rd International Conference on Engineering Applications of Neural Networks, EANN 2022, held in Chersonisos, Crete, Greece, in June 2022. The 37 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on Bio inspired Modeling / Novel Neural Architectures; Classification / Clustering; Machine Learning; Convolutional / Deep Learning; Datamining / Learning / Autoencoders; Deep Learning / Blockchain; Machine Learning for Medical Images / Genome Classification; Reinforcement /Adversarial / Echo State Neural Networks; Robotics / Autonomous Vehicles, Photonic Neural Networks; Text Classification / Natural Language.

Modeling Design and Simulation of Systems

Modeling  Design and Simulation of Systems
Author: Mohamed Sultan Mohamed Ali,Herman Wahid,Nurul Adilla Mohd Subha,Shafishuhaza Sahlan,Mohd Amri Md. Yunus,Ahmad Ridhwan Wahap
Publsiher: Springer
Total Pages: 727
Release: 2017-08-24
ISBN: 9811064636
Category: Computers
Language: EN, FR, DE, ES & NL

Modeling Design and Simulation of Systems Book Excerpt:

This two-volume set CCIS 751 and CCIS 752 constitutes the proceedings of the 17th Asia Simulation Conference, AsiaSim 2017, held in Malacca, Malaysia, in August/September 2017. The 124 revised full papers presented in this two-volume set were carefully reviewed and selected from 267 submissions. The papers contained in these proceedings address challenging issues in modeling and simulation in various fields such as embedded systems; symbiotic simulation; agent-based simulation; parallel and distributed simulation; high performance computing; biomedical engineering; big data; energy, society and economics; medical processes; simulation language and software; visualization; virtual reality; modeling and Simulation for IoT; machine learning; as well as the fundamentals and applications of computing.

Dynamic Modeling of Complex Industrial Processes Data driven Methods and Application Research

Dynamic Modeling of Complex Industrial Processes  Data driven Methods and Application Research
Author: Chao Shang
Publsiher: Springer
Total Pages: 143
Release: 2018-02-22
ISBN: 9811066779
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Dynamic Modeling of Complex Industrial Processes Data driven Methods and Application Research Book Excerpt:

This thesis develops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principle. Using said framework as a point of departure, it then proposes novel strategies for dealing with control monitoring and quality prediction problems in industrial production contexts. The thesis reveals the slowly varying nature of industrial production processes under feedback control, and integrates it with process data analytics to offer powerful prior knowledge that gives rise to statistical methods tailored to industrial data. It addresses several issues of immediate interest in industrial practice, including process monitoring, control performance assessment and diagnosis, monitoring system design, and product quality prediction. In particular, it proposes a holistic and pragmatic design framework for industrial monitoring systems, which delivers effective elimination of false alarms, as well as intelligent self-running by fully utilizing the information underlying the data. One of the strengths of this thesis is its integration of insights from statistics, machine learning, control theory and engineering to provide a new scheme for industrial process modeling in the era of big data.

Bayesian Networks in Fault Diagnosis

Bayesian Networks in Fault Diagnosis
Author: Cai Baoping,Liu Yonghong,Hu Jinqiu
Publsiher: World Scientific
Total Pages: 420
Release: 2018-08-24
ISBN: 9813271507
Category: Mathematics
Language: EN, FR, DE, ES & NL

Bayesian Networks in Fault Diagnosis Book Excerpt:

Fault diagnosis is useful for technicians to detect, isolate, identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This unique compendium presents bibliographical review on the use of BNs in fault diagnosis in the last decades with focus on engineering systems. Subsequently, eleven important issues in BN-based fault diagnosis methodology, such as BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification are discussed in various cases. Researchers, professionals, academics and graduate students will better understand the theory and application, and benefit those who are keen to develop real BN-based fault diagnosis system.

Optimal State Estimation for Process Monitoring Fault Diagnosis and Control

Optimal State Estimation for Process Monitoring  Fault Diagnosis and Control
Author: Ch. Venkateswarlu,Rama Rao Karri
Publsiher: Elsevier
Total Pages: 366
Release: 2022-01-31
ISBN: 0323900682
Category: Computers
Language: EN, FR, DE, ES & NL

Optimal State Estimation for Process Monitoring Fault Diagnosis and Control Book Excerpt:

Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control presents various mechanistic model based state estimators and data-driven model based state estimators with a special emphasis on their development and applications to process monitoring, fault diagnosis and control. The design and analysis of different state estimators are highlighted with a number of applications and case studies concerning to various real chemical and biochemical processes. The book starts with the introduction of basic concepts, extending to classical methods and successively leading to advances in this field. Design and implementation of various classical and advanced state estimation methods to solve a wide variety of problems makes this book immensely useful for the audience working in different disciplines in academics, research and industry in areas concerning to process monitoring, fault diagnosis, control and related disciplines. • Describes various classical and advanced versions of mechanistic model based state estimation algorithms. • Describes various data-driven model based state estimation techniques. • Highlights a number of real applications of mechanistic model based and data-driven model based state estimators/soft sensors. • Beneficial to those associated with process monitoring, fault diagnosis, online optimization, control and related areas.

Parallel and Distributed Computing Applications and Technologies

Parallel and Distributed Computing  Applications and Technologies
Author: Jong Hyuk Park,Hong Shen,Yunsick Sung,Hui Tian
Publsiher: Springer
Total Pages: 484
Release: 2019-02-07
ISBN: 9811359075
Category: Computers
Language: EN, FR, DE, ES & NL

Parallel and Distributed Computing Applications and Technologies Book Excerpt:

This book constitutes the refereed proceedings of the 19th International Conference on CParallel and Distributed Computing, Applications and Technologies, PDCAT 2018, held in Jeju Island, South Korea, in August 2018. The 35 revised full papers presented along with the 14 short papers and were carefully reviewed and selected from 150 submissions. The papers of this volume are organized in topical sections on wired and wireless communication systems, high dimensional data representation and processing, networks and information security, computing techniques for efficient networks design, electronic circuits for communication systems.

Electro Mechanical Actuators for the More Electric Aircraft

Electro Mechanical Actuators for the More Electric Aircraft
Author: Mirko Mazzoleni,Gianpietro Di Rito,Fabio Previdi
Publsiher: Springer Nature
Total Pages: 239
Release: 2021-01-19
ISBN: 3030617998
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Electro Mechanical Actuators for the More Electric Aircraft Book Excerpt:

This book presents recent results on fault diagnosis and condition monitoring of airborne electromechanical actuators, illustrating both algorithmic and hardware design solutions to enhance the reliability of onboard more electric aircraft. The book begins with an introduction to the current trends in the development of electrically powered actuation systems for aerospace applications. Practical examples are proposed to help present approaches to reliability, availability, maintainability and safety analysis of airborne equipment. The terminology and main strategies for fault diagnosis and condition monitoring are then reviewed. The core of the book focuses on the presentation of relevant case studies of fault diagnosis and monitoring design for airborne electromechanical actuators, using different techniques. The last part of the book is devoted to a summary of lessons learned and practical suggestions for the design of fault diagnosis solutions of complex airborne systems. The book is written with the idea of providing practical guidelines on the development of fault diagnosis and monitoring algorithms for airborne electromechanical actuators. It will be of interest to practitioners in aerospace, mechanical, electronic, reliability and systems engineering, as well as researchers and postgraduates interested in dynamical systems, automatic control and safety-critical systems. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Applications of Artificial Intelligence in Process Systems Engineering

Applications of Artificial Intelligence in Process Systems Engineering
Author: Jingzheng Ren,Weifeng Shen,Yi Man,Lichun DOng
Publsiher: Elsevier
Total Pages: 540
Release: 2021-06-05
ISBN: 012821743X
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Applications of Artificial Intelligence in Process Systems Engineering Book Excerpt:

Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis Gives direction to future development trends of AI technologies in chemical and process engineering

Reliability Analysis and Asset Management of Engineering Systems

Reliability Analysis and Asset Management of Engineering Systems
Author: Gilberto Francisco Martha de Souza,Escola Politécnica da USP,Arthur Henrique De Andrade Melani,Miguel Angelo De Carvalho Michalski,Renan Favarao Da Silva
Publsiher: Elsevier
Total Pages: 318
Release: 2021-09-24
ISBN: 0128235225
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Reliability Analysis and Asset Management of Engineering Systems Book Excerpt:

Reliability Analysis and Asset Management of Engineering Systems explains methods that can be used to evaluate reliability and availability of complex systems, including simulation-based methods. The increasing digitization of mechanical processes driven by Industry 4.0 increases the interaction between machines and monitoring and control systems, leading to increases in system complexity. For those systems the reliability and availability analyses are increasingly challenging, as the interaction between machines has become more complex, and the analysis of the flexibility of the production systems to respond to machinery failure may require advanced simulation techniques. This book fills a gap on how to deal with such complex systems by linking the concepts of systems reliability and asset management, and then making these solutions more accessible to industry by explaining the availability analysis of complex systems based on simulation methods that emphasise Petri nets. Explains how to use a monitoring database to perform important tasks including an update of complex systems reliability Shows how to diagnose probable machinery-based causes of system performance degradation by using a monitoring database and reliability estimates in an integrated way Describes practical techniques for the application of AI and machine learning methods to fault detection and diagnosis problems