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

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 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 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

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.

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:

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: 664
Release: 2020
ISBN 10: 3662620049
ISBN 13: 9783662620045
Language: EN, FR, DE, ES & NL

Advanced Methods for Fault Diagnosis and Fault tolerant Control Book Review:

After the first two books have been dedicated to model-based and data-driven fault diagnosis respectively, this book addresses topics in both model-based and data-driven thematic fields with considerable focuses on fault-tolerant control issues and application of machine learning methods. The major objective of the book is to study basic fault diagnosis and fault-tolerant control problems and to build a framework for long-term research efforts in the fault diagnosis and fault-tolerant control domain. In this framework, possibly unified solutions and methods can be developed for general classes of systems. The book is composed of six parts. Besides Part I serving as a common basis for the subsequent studies, Parts II - VI are dedicated to five different thematic areas, including model-based fault diagnosis methods for linear time-varying systems, nonlinear systems and systems with model uncertainties, statistical and data-driven fault diagnosis methods, assessment of fault diagnosis systems, as well as fault-tolerant control with a strong focus on performance degradation monitoring and recovering. These parts are self-contained and so structured that they can also be used for self-study on the concerned topics. The content Basic requirements on fault detection and estimation Basic methods for fault detection and estimation in static and dynamic processes Feedback control, observer, and residual generation Fault detection and estimation for linear time-varying systems Detection and isolation of multiplicative faults in uncertain systems Analysis, parameterisation and optimal design of nonlinear observer-based fault detection systems Data-driven fault detection methods for large-scale and distributed systems Alternative test statistics and data-driven fault detection methods Application of randomised algorithms to assessment and design of fault diagnosis systems Performance-based fault-tolerant control Performance degradation monitoring and recovering Data-driven fault-tolerant control schemes The target groups This book would be valuable for graduate and PhD students as well as for researchers and engineers in the field. The author Prof. Dr.-Ing. Steven X. Ding is a professor and the head of the Institute for Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Germany. His research interests are model-based and data-driven fault diagnosis, control and fault-tolerant systems as well as their applications in industry with a focus on automotive systems, chemical processes and renewable energy systems.

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 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.

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.

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 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.

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.

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.

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

Fault Diagnosis Systems

Fault Diagnosis Systems
Author: Rolf Isermann
Publsiher: Springer Science & Business Media
Total Pages: 475
Release: 2006-01-16
ISBN 10: 3540303685
ISBN 13: 9783540303688
Language: EN, FR, DE, ES & NL

Fault Diagnosis Systems Book Review:

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.

Data Mining Algorithms for Decentralized Fault Detection and Diagnostic in Industrial Systems

Data Mining Algorithms for Decentralized Fault Detection and Diagnostic in Industrial Systems
Author: Mihajlo Grbovic
Publsiher: Unknown
Total Pages: 157
Release: 2012
ISBN 10: 1928374650XXX
ISBN 13: OCLC:1285300519
Language: EN, FR, DE, ES & NL

Data Mining Algorithms for Decentralized Fault Detection and Diagnostic in Industrial Systems Book Review:

Timely Fault Detection and Diagnosis in complex manufacturing systems is critical to ensure safe and effective operation of plant equipment. Process fault is defined as a deviation from normal process behavior, defined within the limits of safe production. The quantifiable objectives of Fault Detection include achieving low detection delay time, low false positive rate, and high detection rate. Once a fault has been detected pinpointing the type of fault is needed for purposes of fault mitigation and returning to normal process operation. This is known as Fault Diagnosis. Data-driven Fault Detection and Diagnosis methods emerged as an attractive alternative to traditional mathematical model-based methods, especially for complex systems due to difficulty in describing the underlying process. A distinct feature of data-driven methods is that no a priori information about the process is necessary. Instead, it is assumed that historical data, containing process features measured in regular time intervals (e.g., power plant sensor measurements), are available for development of fault detection/diagnosis model through generalization of data. The goal of my research was to address the shortcomings of the existing data-driven methods and contribute to solving open problems, such as: 1) decentralized fault detection and diagnosis; 2) fault detection in the cold start setting; 3) optimizing the detection delay and dealing with noisy data annotations. 4) developing models that can adapt to concept changes in power plant dynamics. For small-scale sensor networks, it is reasonable to assume that all measurements are available at a central location (sink) where fault predictions are made. This is known as a centralized fault detection approach. For large-scale networks, decentralized approach is often used, where network is decomposed into potentially overlapping blocks and each block provides local decisions that are fused at the sink. The appealing properties of the decentralized approach include fault tolerance, scalability, and reusability. When one or more blocks go offline due to maintenance of their sensors, the predictions can still be made using the remaining blocks. In addition, when the physical facility is reconfigured, either by changing its components or sensors, it can be easier to modify part of the decentralized system impacted by the changes than to overhaul the whole centralized system. The scalability comes from reduced costs of system setup, update, communication, and decision making. Main challenges in decentralized monitoring include process decomposition and decision fusion. We proposed a decentralized model where the sensors are partitioned into small, potentially overlapping, blocks based on the Sparse Principal Component Analysis (PCA) algorithm, which preserves strong correlations among sensors, followed by training local models at each block, and fusion of decisions based on the proposed Maximum Entropy algorithm. Moreover, we introduced a novel framework for adding constraints to the Sparse PCA problem. The constraints limit the set of possible solutions by imposing additional goals to be reached trough optimization along with the existing Sparse PCA goals. The experimental results on benchmark fault detection data show that Sparse PCA can utilize prior knowledge, which is not directly available in data, in order to produce desirable network partitions, with a pre-defined limit on communication cost and/or robustness.

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 10: 9811066779
ISBN 13: 9789811066771
Language: EN, FR, DE, ES & NL

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

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.

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 10: 9811064636
ISBN 13: 9789811064630
Language: EN, FR, DE, ES & NL

Modeling Design and Simulation of Systems Book Review:

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.