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 10: 0128191651
ISBN 13: 9780128191651
Language: EN, FR, DE, ES & NL

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

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

Data driven Methods for Fault Detection and Diagnosis in Chemical Processes

Data driven Methods for Fault Detection and Diagnosis in Chemical Processes
Author: Evan L. Russell,Leo H. Chiang,Richard D. Braatz
Publsiher: Springer Science & Business Media
Total Pages: 192
Release: 2012-12-06
ISBN 10: 1447104099
ISBN 13: 9781447104094
Language: EN, FR, DE, ES & NL

Data driven Methods for Fault Detection and Diagnosis in Chemical Processes Book Review:

Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data. The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process-monitoring techniques presented include: Principal component analysis; Fisher discriminant analysis; Partial least squares; Canonical variate analysis. The text demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process-monitoring techniques to a nontrivial simulated process, and can compare their performance with that obtained in the case studies in the text are included. A number of additional homework problems encourage the reader to implement and obtain a deeper understanding of the techniques. The reader will obtain a background in data-driven techniques for fault detection and diagnosis, including the ability to implement the techniques and to know how to select the right technique for a particular application.

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 10: 1447164105
ISBN 13: 9781447164104
Language: EN, FR, DE, ES & NL

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

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.

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 10: 1119882311
ISBN 13: 9781119882312
Language: EN, FR, DE, ES & NL

Diagnosis and Fault tolerant Control 1 Book Review:

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 10: 9780128191644
ISBN 13: 0128191643
Language: EN, FR, DE, ES & NL

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

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

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: 2012-12-06
ISBN 10: 1447103475
ISBN 13: 9781447103479
Language: EN, FR, DE, ES & NL

Fault Detection and Diagnosis in Industrial Systems Book Review:

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.

Model Based Fault Diagnosis Techniques

Model Based Fault Diagnosis Techniques
Author: Steven X. Ding
Publsiher: Springer Science & Business Media
Total Pages: 504
Release: 2012-12-20
ISBN 10: 1447147995
ISBN 13: 9781447147992
Language: EN, FR, DE, ES & NL

Model Based Fault Diagnosis Techniques Book Review:

Guaranteeing a high system performance over a wide operating range is an important issue surrounding the design of automatic control systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification (FDI) is receiving considerable attention. This book introduces basic model-based FDI schemes, advanced analysis and design algorithms, and mathematical and control-theoretic tools. This second edition of Model-Based Fault Diagnosis Techniques contains: • new material on fault isolation and identification and alarm management; • extended and revised treatment of systematic threshold determination for systems with both deterministic unknown inputs and stochastic noises; • addition of the continuously-stirred tank heater as a representative process-industrial benchmark; and • enhanced discussion of residual evaluation which now deals with stochastic processes. Model-based Fault Diagnosis Techniques will interest academic researchers working in fault identification and diagnosis and as a text it is suitable for graduate students in a formal university-based course or as a self-study aid for practising engineers working with automatic control or mechatronic systems from backgrounds as diverse as chemical process and power engineering.

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 10: 3658167564
ISBN 13: 9783658167561
Language: EN, FR, DE, ES & NL

Data Driven Fault Detection for Industrial Processes Book Review:

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 10: 1928374650XXX
ISBN 13: OCLC:1101444770
Language: EN, FR, DE, ES & NL

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

Data Driven Design of Fault Diagnosis Systems

Data Driven Design of Fault Diagnosis Systems
Author: Adel Haghani Abandan Sari
Publsiher: Springer Science & Business
Total Pages: 136
Release: 2014-04-22
ISBN 10: 3658058072
ISBN 13: 9783658058074
Language: EN, FR, DE, ES & NL

Data Driven Design of Fault Diagnosis Systems Book Review:

In many industrial applications early detection and diagnosis of abnormal behavior of the plant is of great importance. During the last decades, the complexity of process plants has been drastically increased, which imposes great challenges in development of model-based monitoring approaches and it sometimes becomes unrealistic for modern large-scale processes. The main objective of Adel Haghani Abandan Sari is to study efficient fault diagnosis techniques for complex industrial systems using process historical data and considering the nonlinear behavior of the process. To this end, different methods are presented to solve the fault diagnosis problem based on the overall behavior of the process and its dynamics. Moreover, a novel technique is proposed for fault isolation and determination of the root-cause of the faults in the system, based on the fault impacts on the process measurements.

Advanced methods for fault diagnosis and fault tolerant control

Advanced methods for fault diagnosis and fault tolerant control
Author: Steven X. Ding
Publsiher: Springer
Total Pages: 658
Release: 2020-11-24
ISBN 10: 9783662620038
ISBN 13: 3662620030
Language: EN, FR, DE, ES & NL

Advanced methods for fault diagnosis and fault tolerant control Book Review:

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 Fault Detection and Reasoning for Industrial Monitoring

Data Driven Fault Detection and Reasoning for Industrial Monitoring
Author: Jing Wang,Jinglin Zhou,Xiaolu Chen
Publsiher: Springer
Total Pages: 264
Release: 2022-01-04
ISBN 10: 9789811680434
ISBN 13: 9811680434
Language: EN, FR, DE, ES & NL

Data Driven Fault Detection and Reasoning for Industrial Monitoring Book Review:

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book.

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 10: 3662620049
ISBN 13: 9783662620045
Language: EN, FR, DE, ES & NL

Advanced methods for fault diagnosis and fault tolerant control Book Review:

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 Fault Detection for Industrial Processes

Data Driven Fault Detection for Industrial Processes
Author: Zhiwen Chen
Publsiher: Springer Vieweg
Total Pages: 112
Release: 2017-01-12
ISBN 10: 9783658167554
ISBN 13: 3658167556
Language: EN, FR, DE, ES & NL

Data Driven Fault Detection for Industrial Processes Book Review:

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.

Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery

Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery
Author: Yaguo Lei
Publsiher: Butterworth-Heinemann
Total Pages: 376
Release: 2016-11-02
ISBN 10: 0128115351
ISBN 13: 9780128115350
Language: EN, FR, DE, ES & NL

Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery Book Review:

Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery provides a comprehensive introduction of intelligent fault diagnosis and RUL prediction based on the current achievements of the author's research group. The main contents include multi-domain signal processing and feature extraction, intelligent diagnosis models, clustering algorithms, hybrid intelligent diagnosis strategies, and RUL prediction approaches, etc. This book presents fundamental theories and advanced methods of identifying the occurrence, locations, and degrees of faults, and also includes information on how to predict the RUL of rotating machinery. Besides experimental demonstrations, many application cases are presented and illustrated to test the methods mentioned in the book. This valuable reference provides an essential guide on machinery fault diagnosis that helps readers understand basic concepts and fundamental theories. Academic researchers with mechanical engineering or computer science backgrounds, and engineers or practitioners who are in charge of machine safety, operation, and maintenance will find this book very useful. Provides a detailed background and roadmap of intelligent diagnosis and RUL prediction of rotating machinery, involving fault mechanisms, vibration characteristics, health indicators, and diagnosis and prognostics Presents basic theories, advanced methods, and the latest contributions in the field of intelligent fault diagnosis and RUL prediction Includes numerous application cases, and the methods, algorithms, and models introduced in the book are demonstrated by industrial experiences

Model based Fault Diagnosis in Dynamic Systems Using Identification Techniques

Model based Fault Diagnosis in Dynamic Systems Using Identification Techniques
Author: Silvio Simani,Cesare Fantuzzi,Ron J. Patton
Publsiher: Springer Science & Business Media
Total Pages: 282
Release: 2013-11-11
ISBN 10: 1447138295
ISBN 13: 9781447138297
Language: EN, FR, DE, ES & NL

Model based Fault Diagnosis in Dynamic Systems Using Identification Techniques Book Review:

Safety in industrial process and production plants is a concern of rising importance but because the control devices which are now exploited to improve the performance of industrial processes include both sophisticated digital system design techniques and complex hardware, there is a higher probability of failure. Control systems must include automatic supervision of closed-loop operation to detect and isolate malfunctions quickly. A promising method for solving this problem is "analytical redundancy", in which residual signals are obtained and an accurate model of the system mimics real process behaviour. If a fault occurs, the residual signal is used to diagnose and isolate the malfunction. This book focuses on model identification oriented to the analytical approach of fault diagnosis and identification covering: choice of model structure; parameter identification; residual generation; and fault diagnosis and isolation. Sample case studies are used to demonstrate the application of these techniques.

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 10: 3030462633
ISBN 13: 9783030462635
Language: EN, FR, DE, ES & NL

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

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.

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 10: 3036504621
ISBN 13: 9783036504629
Language: EN, FR, DE, ES & NL

Algorithms for Fault Detection and Diagnosis Book Review:

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.

Fault Detection Supervision and Safety of Technical Processes 2006

Fault Detection  Supervision and Safety of Technical Processes 2006
Author: Hong-Yue Zhang
Publsiher: Elsevier
Total Pages: 1576
Release: 2007-03-01
ISBN 10: 9780080555393
ISBN 13: 008055539X
Language: EN, FR, DE, ES & NL

Fault Detection Supervision and Safety of Technical Processes 2006 Book Review:

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

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 10: 0128193662
ISBN 13: 9780128193662
Language: EN, FR, DE, ES & NL

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

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