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: 2020-07-29
ISBN 10: 0128193654
ISBN 13: 9780128193655
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

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

Road Traffic Modeling and Management

Road Traffic Modeling and Management
Author: Fouzi Harrou,Abdelhafid Zeroual,Mohamad Mazen Hittawe,Ying Sun
Publsiher: Elsevier
Total Pages: 268
Release: 2021-10-15
ISBN 10: 0128234334
ISBN 13: 9780128234334
Language: EN, FR, DE, ES & NL

Road Traffic Modeling and Management Book Review:

Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing road traffic monitoring and management. The book examines commonly used traffic analysis methodologies as well the emerging methods that use deep learning methods. Other sections discuss how to understand statistical models and machine learning algorithms and how to apply them to traffic modeling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, this book is ideal for researchers and practitioners who want to improve the performance of intelligent transportation systems. Provides integrated, up-to-date and complete coverage of the key components for intelligent transportation systems: traffic modeling, forecasting, estimation and monitoring Uses methods based on video and time series data for traffic modeling and forecasting Includes case studies, key processes guidance and comparisons of different methodologies

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
Author: Chris Aldrich,Lidia Auret
Publsiher: Springer Science & Business Media
Total Pages: 374
Release: 2013-06-15
ISBN 10: 1447151852
ISBN 13: 9781447151852
Language: EN, FR, DE, ES & NL

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods Book Review:

This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.

Advanced Systems for Biomedical Applications

Advanced Systems for Biomedical Applications
Author: Olfa Kanoun
Publsiher: Springer Nature
Total Pages: 135
Release: 2021
ISBN 10: 3030712214
ISBN 13: 9783030712211
Language: EN, FR, DE, ES & NL

Advanced Systems for Biomedical Applications 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: 135
Release: 2021
ISBN 10: 3662620049
ISBN 13: 9783662620045
Language: EN, FR, DE, ES & NL

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

Multivariate Statistical Process Control

Multivariate Statistical Process Control
Author: Zhiqiang Ge,Zhihuan Song
Publsiher: Springer Science & Business Media
Total Pages: 194
Release: 2012-11-28
ISBN 10: 1447145135
ISBN 13: 9781447145134
Language: EN, FR, DE, ES & NL

Multivariate Statistical Process Control Book Review:

Given their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Multivariate statistical process control (MSPC) is one of the most popular data-based methods for process monitoring and is widely used in various industrial areas. Effective routines for process monitoring can help operators run industrial processes efficiently at the same time as maintaining high product quality. Multivariate Statistical Process Control reviews the developments and improvements that have been made to MSPC over the last decade, and goes on to propose a series of new MSPC-based approaches for complex process monitoring. These new methods are demonstrated in several case studies from the chemical, biological, and semiconductor industrial areas. Control and process engineers, and academic researchers in the process monitoring, process control and fault detection and isolation (FDI) disciplines will be interested in this book. It can also be used to provide supplementary material and industrial insight for graduate and advanced undergraduate students, and graduate engineers. Advances in Industrial Control aims to report and encourage 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.

Diagnostic Techniques in Industrial Engineering

Diagnostic Techniques in Industrial Engineering
Author: Mangey Ram,J. Paulo Davim
Publsiher: Springer
Total Pages: 247
Release: 2017-10-20
ISBN 10: 3319654977
ISBN 13: 9783319654973
Language: EN, FR, DE, ES & NL

Diagnostic Techniques in Industrial Engineering Book Review:

This book presents the most important tools, techniques, strategy and diagnostic methods used in industrial engineering. The current widely accepted methods of diagnosis and their properties are discussed. Also, the possible fruitful areas for further research in the field are identified.

Advances in Production Management Systems Production Management for Data Driven Intelligent Collaborative and Sustainable Manufacturing

Advances in Production Management Systems  Production Management for Data Driven  Intelligent  Collaborative  and Sustainable Manufacturing
Author: Ilkyeong Moon,Gyu M. Lee,Jinwoo Park,Dimitris Kiritsis,Gregor von Cieminski
Publsiher: Springer
Total Pages: 570
Release: 2018-08-24
ISBN 10: 3319997041
ISBN 13: 9783319997049
Language: EN, FR, DE, ES & NL

Advances in Production Management Systems Production Management for Data Driven Intelligent Collaborative and Sustainable Manufacturing Book Review:

The two-volume set IFIP AICT 535 and 536 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2018, held in Seoul, South Korea, in August 2018. The 129 revised full papers presented were carefully reviewed and selected from 149 submissions. They are organized in the following topical sections: lean and green manufacturing; operations management in engineer-to-order manufacturing; product-service systems, customer-driven innovation and value co-creation; collaborative networks; smart production for mass customization; global supply chain management; knowledge based production planning and control; knowledge based engineering; intelligent diagnostics and maintenance solutions for smart manufacturing; service engineering based on smart manufacturing capabilities; smart city interoperability and cross-platform implementation; manufacturing performance management in smart factories; industry 4.0 - digital twin; industry 4.0 - smart factory; and industry 4.0 - collaborative cyber-physical production and human systems.

Practical Design of Ships and Other Floating Structures

Practical Design of Ships and Other Floating Structures
Author: Tetsuo Okada,Katsuyuki Suzuki,Yasumi Kawamura
Publsiher: Springer Nature
Total Pages: 845
Release: 2020-10-03
ISBN 10: 981154672X
ISBN 13: 9789811546723
Language: EN, FR, DE, ES & NL

Practical Design of Ships and Other Floating Structures Book Review:

This book gathers the peer-reviewed proceedings of the 14th International Symposium, PRADS 2019, held in Yokohama, Japan, in September 2019. It brings together naval architects, engineers, academic researchers and professionals who are involved in ships and other floating structures to share the latest research advances in the field. The contents cover a broad range of topics, including design synthesis for ships and floating systems, production, hydrodynamics, and structures and materials. Reflecting the latest advances, the book will be of interest to researchers and practitioners alike.

Computational Science and Its Applications ICCSA 2019

Computational Science and Its Applications     ICCSA 2019
Author: Sanjay Misra,Osvaldo Gervasi,Beniamino Murgante,Elena Stankova,Vladimir Korkhov,Carmelo Torre,Ana Maria A.C. Rocha,David Taniar,Bernady O. Apduhan,Eufemia Tarantino
Publsiher: Springer
Total Pages: 845
Release: 2019-06-28
ISBN 10: 3030242897
ISBN 13: 9783030242893
Language: EN, FR, DE, ES & NL

Computational Science and Its Applications ICCSA 2019 Book Review:

The six volumes LNCS 11619-11624 constitute the refereed proceedings of the 19th International Conference on Computational Science and Its Applications, ICCSA 2019, held in Saint Petersburg, Russia, in July 2019. The 64 full papers, 10 short papers and 259 workshop papers presented were carefully reviewed and selected form numerous submissions. The 64 full papers are organized in the following five general tracks: computational methods, algorithms and scientific applications; high performance computing and networks; geometric modeling, graphics and visualization; advanced and emerging applications; and information systems and technologies. The 259 workshop papers were presented at 33 workshops in various areas of computational sciences, ranging from computational science technologies to specific areas of computational sciences, such as software engineering, security, artificial intelligence and blockchain technologies.

Smart Monitoring of Rotating Machinery for Industry 4 0

Smart Monitoring of Rotating Machinery for Industry 4 0
Author: Fakher Chaari
Publsiher: Springer Nature
Total Pages: 135
Release: 2021
ISBN 10: 3030795195
ISBN 13: 9783030795191
Language: EN, FR, DE, ES & NL

Smart Monitoring of Rotating Machinery for Industry 4 0 Book Review:

Advanced Information Systems Engineering Workshops

Advanced Information Systems Engineering Workshops
Author: Artem Polyvyanyy
Publsiher: Springer Nature
Total Pages: 135
Release: 2021
ISBN 10: 3030790223
ISBN 13: 9783030790226
Language: EN, FR, DE, ES & NL

Advanced Information Systems Engineering Workshops Book Review:

Time Series Analysis

Time Series Analysis
Author: Chun-Kit Ngan
Publsiher: BoD – Books on Demand
Total Pages: 130
Release: 2019-11-06
ISBN 10: 1789847788
ISBN 13: 9781789847789
Language: EN, FR, DE, ES & NL

Time Series Analysis Book Review:

This book aims to provide readers with the current information, developments, and trends in a time series analysis, particularly in time series data patterns, technical methodologies, and real-world applications. This book is divided into three sections and each section includes two chapters. Section 1 discusses analyzing multivariate and fuzzy time series. Section 2 focuses on developing deep neural networks for time series forecasting and classification. Section 3 describes solving real-world domain-specific problems using time series techniques. The concepts and techniques contained in this book cover topics in time series research that will be of interest to students, researchers, practitioners, and professors in time series forecasting and classification, data analytics, machine learning, deep learning, and artificial intelligence.

Women in Industrial and Systems Engineering

Women in Industrial and Systems Engineering
Author: Alice E. Smith
Publsiher: Springer Nature
Total Pages: 609
Release: 2019-09-13
ISBN 10: 3030118665
ISBN 13: 9783030118662
Language: EN, FR, DE, ES & NL

Women in Industrial and Systems Engineering Book Review:

This book presents a diversity of innovative and impactful research in the field of industrial and systems engineering (ISE) led by women investigators. After a Foreword by Margaret L. Brandeau, an eminent woman scholar in the field, the book is divided into the following sections: Analytics, Education, Health, Logistics, and Production. Also included is a comprehensive biography on the historic luminary of industrial engineering, Lillian Moeller Gilbreth. Each chapter presents an opportunity to learn about the impact of the field of industrial and systems engineering and women’s important contributions to it. Topics range from big data analysis, to improving cancer treatment, to sustainability in product design, to teamwork in engineering education. A total of 24 topics touch on many of the challenges facing the world today and these solutions by women researchers are valuable for their technical innovation and excellence and their non-traditional perspective. Found within each author’s biography are their motivations for entering the field and how they view their contributions, providing inspiration and guidance to those entering industrial engineering.

Advanced Mapping of Environmental Data

Advanced Mapping of Environmental Data
Author: Mikhail Kanevski
Publsiher: John Wiley & Sons
Total Pages: 352
Release: 2013-05-10
ISBN 10: 1118623266
ISBN 13: 9781118623268
Language: EN, FR, DE, ES & NL

Advanced Mapping of Environmental Data Book Review:

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.

Data Analytics Applied to the Mining Industry

Data Analytics Applied to the Mining Industry
Author: Ali Soofastaei
Publsiher: CRC Press
Total Pages: 254
Release: 2020-11-13
ISBN 10: 0429781768
ISBN 13: 9780429781766
Language: EN, FR, DE, ES & NL

Data Analytics Applied to the Mining Industry Book Review:

Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes critical opportunity areas for mining optimization Brings experience and learning in digital transformation from adjacent sectors

Advanced Contemporary Control

Advanced  Contemporary Control
Author: Andrzej Bartoszewicz,Jacek Kabziński,Janusz Kacprzyk
Publsiher: Springer Nature
Total Pages: 1568
Release: 2020-06-24
ISBN 10: 3030509362
ISBN 13: 9783030509361
Language: EN, FR, DE, ES & NL

Advanced Contemporary Control Book Review:

This book presents the proceedings of the 20th Polish Control Conference. A triennial event that was first held in 1958, the conference successfully combines its long tradition with a modern approach to shed light on problems in control engineering, automation, robotics and a wide range of applications in these disciplines. The book presents new theoretical results concerning the steering of dynamical systems, as well as industrial case studies and worked solutions to real-world problems in contemporary engineering. It particularly focuses on the modelling, identification, analysis and design of automation systems; however, it also addresses the evaluation of their performance, efficiency and reliability. Other topics include fault-tolerant control in robotics, automated manufacturing, mechatronics and industrial systems. Moreover, it discusses data processing and transfer issues, covering a variety of methodologies, including model predictive, robust and adaptive techniques, as well as algebraic and geometric methods, and fractional order calculus approaches. The book also examines essential application areas, such as transportation and autonomous intelligent vehicle systems, robotic arms, mobile manipulators, cyber-physical systems, electric drives and both surface and underwater marine vessels. Lastly, it explores biological and medical applications of the control-theory-inspired methods.

Digitalization and Analytics for Smart Plant Performance

Digitalization and Analytics for Smart Plant Performance
Author: Frank (Xin X.) Zhu
Publsiher: John Wiley & Sons
Total Pages: 544
Release: 2021-04-06
ISBN 10: 1119634113
ISBN 13: 9781119634119
Language: EN, FR, DE, ES & NL

Digitalization and Analytics for Smart Plant Performance Book Review:

This book addresses the topic of integrated digitization of plants on an objective basis and in a holistic manner by sharing data, applying analytics tools and integrating workflows via pertinent examples from industry. It begins with an evaluation of current performance management practices and an overview of the need for a "Connected Plant" via digitalization followed by sections on "Connected Assets: Improve Reliability and Utilization," "Connected Processes: Optimize Performance and Economic Margin " and "Connected People: Digitalizing the Workforce and Workflows and Developing Ownership and Digital Culture," then culminating in a final section entitled "Putting All Together Into an Intelligent Digital Twin Platform for Smart Operations and Demonstrated by Application cases."

Data Driven Decision Making Under Uncertainty for Intelligent Life cycle Control of the Built Environment

Data Driven Decision Making Under Uncertainty for Intelligent Life cycle Control of the Built Environment
Author: Charalampos Andriotis
Publsiher: Unknown
Total Pages: 135
Release: 2019
ISBN 10: 1928374650XXX
ISBN 13: OCLC:1117333600
Language: EN, FR, DE, ES & NL

Data Driven Decision Making Under Uncertainty for Intelligent Life cycle Control of the Built Environment Book Review:

This dissertation provides novel frameworks for data-driven probabilistic performance-based assessments and optimal or near-optimal stochastic control strategies for structural, infrastructural and other engineering systems. The goal of this research is to enable efficient and robust structural performance predictions and optimized decisions over the entire operating life of systems, by developing advanced statistical learning models, machine learning formulations and Artificial Intelligence (AI) algorithms, in order to contribute to a future of smart and sustainable infrastructure. To this end, the developed approaches build upon and extend well-established statistical modeling frameworks, infuse intelligence to structural informatics through newly introduced schemes for structural data mining and processing, provide comprehensive solutions to challenging life-cycle objectives, and support complex decisions in previously intractable sequential decision-making problems through novel AI-aided algorithms and theoretical concepts.Efficient assessment of various societal, environmental and economic losses necessitates adept statistical and learning models, able to consistently capture longitudinal dependencies in data and translate multivariate information in structural condition and performance metrics. This dissertation addresses this need, within a softmax regression fragility analysis framework that avoids fragility function crossing inconsistencies and scales well in high-dimensional intensity measure spaces with multiple structural states. Moreover, softmax-based fragility functions are generalized by advanced statistical learning and deep learning formulations that employ Dynamic Bayesian Networks (DBNs), in the form of Dependent Markov Models (DMMs) and Dependent Hidden Markov Models (DHMMs), as well as Recurrent Neural Network (RNN) architectures. The above considerably extend and generalize the framework of probabilistic performance engineering, with theoretically consistent multi-state multi-variate fragility functions, which also have multi-step predictive capabilities in time. The hidden spaces of DHMMs and RNNs are shown to be able to encode noisy input to noisy output sequences through structured hidden spaces. It turns out that the Markovian properties of these spaces can portray damage-consistent dynamics, whereas they are directly pertinent to the input required in advanced decision frameworks that employ Markovian processes for decision-making either under full, partial, or mixed observability assumptions.Hidden Markov models equipped with costs and control actions can provide a theoretically neat and computationally robust framework for sequential decision-making problems under uncertainty, through Partially Observable Markov Decision Processes (POMDPs). This research casts stochastic control problems for determination of optimal or near-optimal life-cycle maintenance and inspection strategies within the premises of POMDPs. Specialized formulations of full or mixed observability are also developed, through Markov Decision Processes (MDPs) or Mixed Observability Markov Decision Processes (MOMDPs), respectively. Along these lines, this research enables decision-support systems which can operate in stochastic engineering environments with uncertain action outcomes and noisy real-time observations, having global optimality guarantees as a result of the relevant underlying dynamic programming formulations introduced and, in many cases, well-defined performance bounds. In the same vein, the Value of Information (VoI) and the Value of Structural Health Monitoring (VoSHM) are quantified and a straightforward definition for the expected life-cycle gains of different observational and monitoring options is established and evaluated. Formulating VoI and VoSHM within the framework of POMDPs, the estimates of these metrics depict value gaps between the optimal life-cycle strategies of the examined options, thus also being able to provide bounds on the respective gains.For small- to medium-scale systems, solutions to the life-cycle optimization problems are derived by point-based solution schemes which provide efficient exploration heuristics, value function updates over the POMDP belief-space, vector compression techniques and convergence properties. For large-scale multi-component engineering systems that form large state and action spaces, such point-based schemes are however impractical as they require explicit prior information of the system dynamics model. To this end, the Deep Centralized Multi-agent Actor Critic (DCMAC) is developed herein and implemented in the solution procedure. DCMAC is an efficient off-policy actor-critic Deep Reinforcement Learning (DRL) algorithm with experience replay. DCMAC alleviates the curse of dimensionality related to state, observation and actions spaces of multi-component systems through deep network approximators and a factorized representation of the actor. DCMAC interacts directly with the simulator, thus avoiding the need for full and explicit model-based knowledge of the system dynamics, and operates in the POMDP belief space, by encoding sequences of actions and observations in belief vectors through Bayesian updates. Overall, DCMAC is able to efficiently tackle the state and action space scalability issues, as well as the potential model unavailability at the system level, all of which often make the decision problems of large multi-component systems hard to solve, if not intractable, by conventional machine learning schemes and other life-cycle optimization methodologies.All developed methods and frameworks are rigorously evaluated in relevant numerical applications and their strengths, limitations and broader capabilities are highlighted and discussed. Results demonstrate the effectiveness of the proposed models, solution procedures and algorithmic schemes, in enabling efficient data-driven probabilistic predictions and structural informatics, as well as comprehensive optimal or near-optimal stochastic control strategies for engineering systems. Overall, the originally developed statistical and machine learning models, in conjunction with the dedicated AI-aided algorithms, can ensure advanced and sophisticated solutions and open numerous new scientific paths towards smart cities, intelligent infrastructure, and autonomous control of the built environment.