Statistical Process Monitoring Using Advanced Data Driven And Deep Learning Approaches
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Statistical Process Monitoring Using Advanced Data Driven And Deep Learning Approaches
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Author : Fouzi Harrou
language : en
Publisher: Elsevier
Release Date : 2020-07-03
Statistical Process Monitoring Using Advanced Data Driven And Deep Learning Approaches written by Fouzi Harrou and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-03 with Technology & Engineering categories.
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
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Author : Fouzi Harrou
language : en
Publisher: Elsevier
Release Date : 2021-10-05
Road Traffic Modeling And Management written by Fouzi Harrou and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-05 with Transportation categories.
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
Recent Developments In Model Based And Data Driven Methods For Advanced Control And Diagnosis
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Author : Didier Theilliol
language : en
Publisher: Springer Nature
Release Date : 2023-07-15
Recent Developments In Model Based And Data Driven Methods For Advanced Control And Diagnosis written by Didier Theilliol and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-15 with Technology & Engineering categories.
The book consists of recent works on several axes either with a more theoretical nature or with a focus on applications, which will span a variety of up-to-date topics in the field of systems and control. The main market area of the contributions include: Advanced fault-tolerant control, control reconfiguration, health monitoring techniques for industrial systems, data-driven diagnosis methods, process supervision, diagnosis and control of discrete-event systems, maintenance and repair strategies, statistical methods for fault diagnosis, reliability and safety of industrial systems artificial intelligence methods for control and diagnosis, health-aware control design strategies, advanced control approaches, deep learning-based methods for control and diagnosis, reinforcement learning-based approaches for advanced control, diagnosis and prognosis techniques applied to industrial problems, Industry 4.0 as well as instrumentation and sensors. These works constitute advances in the aforementioned scientific fields and will be used by graduate as well as doctoral students along with established researchers to update themselves with the state of the art and recent advances in their respective fields. As the book includes several applicative studies with several multi-disciplinary contributions (deep learning, reinforcement learning, model-based/data-based control etc.), the book proves to be equally useful for the practitioners as well industrial professionals.
Power Systems Cybersecurity
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Author : Hassan Haes Alhelou
language : en
Publisher: Springer Nature
Release Date : 2023-03-12
Power Systems Cybersecurity written by Hassan Haes Alhelou and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-12 with Technology & Engineering categories.
This book covers power systems cybersecurity. In order to enhance overall stability and security in wide-area cyber-physical power systems and defend against cyberattacks, new resilient operation, control, and protection methods are required. The cyberattack-resilient control methods improve overall cybersecurity and stability in normal and abnormal operating conditions. By contrast, cyberattack-resilient protection schemes are important to keep the secure operation of a system under the most severe contingencies and cyberattacks. The main subjects covered in the book are: 1) proposing new tolerant and cyberattack-resilient control and protection methods against cyberattacks for future power systems, 2) suggesting new methods for cyberattack detection and cybersecurity assessment, and 3) focusing on practical issues in modern power systems.
Machine Learning In Python For Process And Equipment Condition Monitoring And Predictive Maintenance
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Author : Ankur Kumar
language : en
Publisher: MLforPSE
Release Date : 2024-01-12
Machine Learning In Python For Process And Equipment Condition Monitoring And Predictive Maintenance written by Ankur Kumar and has been published by MLforPSE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-12 with Computers categories.
This book is designed to help readers quickly gain a working knowledge of machine learning-based techniques that are widely employed for building equipment condition monitoring, plantwide monitoring , and predictive maintenance solutions in process industry . The book covers a broad spectrum of techniques ranging from univariate control charts to deep learning-based prediction of remaining useful life. Consequently, the readers can leverage the concepts learned to build advanced solutions for fault detection, fault diagnosis, and fault prognosis. The application focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers and data scientists. Upon completion, readers will be able to confidently navigate the Prognostics and Health Management literature and make judicious selection of modeling approaches suitable for their problems. This book has been divided into seven parts. Part 1 lays down the basic foundations of ML-assisted process and equipment condition monitoring, and predictive maintenance. Part 2 provides in-detail presentation of classical ML techniques for univariate signal monitoring. Different types of control charts and time-series pattern matching methodologies are discussed. Part 3 is focused on the widely popular multivariate statistical process monitoring (MSPM) techniques. Emphasis is paid to both the fault detection and fault isolation/diagnosis aspects. Part 4 covers the process monitoring applications of classical machine learning techniques such as k-NN, isolation forests, support vector machines, etc. These techniques come in handy for processes that cannot be satisfactorily handled via MSPM techniques. Part 5 navigates the world of artificial neural networks (ANN) and studies the different ANN structures that are commonly employed for fault detection and diagnosis in process industry. Part 6 focusses on vibration-based monitoring of rotating machinery and Part 7 deals with prognostic techniques for predictive maintenance applications. Broadly, the book covers the following: Exploratory analysis of process data Best practices for process monitoring and predictive maintenance solutions Univariate monitoring via control charts and time series data mining Multivariate statistical process monitoring techniques (PCA, PLS, FDA, etc.) Machine learning and deep learning techniques to handle dynamic, nonlinear, and multimodal processes Fault detection and diagnosis of rotating machinery using vibration data Remaining useful life predictions for predictive maintenance
Proceedings Of Asean Australian Engineering Congress Aaec2022
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Author : Chung Siung Choo
language : en
Publisher: Springer Nature
Release Date : 2023-12-19
Proceedings Of Asean Australian Engineering Congress Aaec2022 written by Chung Siung Choo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-19 with Technology & Engineering categories.
This book presents the proceedings of the ASEAN-Australian Engineering Congress (AAEC2022), held as a virtual event, 13–15 July 2022 with the theme “Engineering Solutions in the Age of Digital Disruption”. The book presents selected papers covering scientific research in the field of Engineering Computing, Network, Communication and Cybersecurity, Artificial Intelligence & Machine Learning, Materials Science & Manufacturing, Automation and Sensors, Smart Energy & Cities, Simulation & Optimisation and other Industry 4.0 related Technologies. The book appeals to researchers, academics, scientists, students, engineers and practitioners who are interested in the latest developments and applications related to addressing the Fourth Industrial Revolution (IR4.0).
Structural Health Monitoring Based On Data Science Techniques
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Author : Alexandre Cury
language : en
Publisher: Springer Nature
Release Date : 2021-10-23
Structural Health Monitoring Based On Data Science Techniques written by Alexandre Cury and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-23 with Computers categories.
The modern structural health monitoring (SHM) paradigm of transforming in situ, real-time data acquisition into actionable decisions regarding structural performance, health state, maintenance, or life cycle assessment has been accelerated by the rapid growth of “big data” availability and advanced data science. Such data availability coupled with a wide variety of machine learning and data analytics techniques have led to rapid advancement of how SHM is executed, enabling increased transformation from research to practice. This book intends to present a representative collection of such data science advancements used for SHM applications, providing an important contribution for civil engineers, researchers, and practitioners around the world.
Machine Learning In Python For Process Systems Engineering
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Author : Ankur Kumar
language : en
Publisher: MLforPSE
Release Date : 2022-02-25
Machine Learning In Python For Process Systems Engineering written by Ankur Kumar and has been published by MLforPSE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-25 with Computers categories.
This book provides an application-focused exposition of modern ML tools that have proven useful in process industry and hands-on illustrations on how to develop ML-based solutions for process monitoring, predictive maintenance, fault diagnosis, inferential modeling, dimensionality reduction, and process control. This book considers unique characteristics of industrial process data and uses real data from industrial systems for illustrations. With the focus on practical implementation and minimal programming or ML prerequisites, the book covers the gap in available ML resources for industrial practitioners. The authors of this book have drawn from their years of experience in developing data-driven industrial solutions to provide a guided tour along the wide range of available ML methods and declutter the world of machine learning. The readers will find all the resources they need to deal with high-dimensional, correlated, noisy, corrupted, multimode, and nonlinear process data. The book has been divided into four parts. Part 1 provides a perspective on the importance of ML in process systems engineering and lays down the basic foundations of ML. Part 2 provides in-detail presentation of classical ML techniques and has been written keeping in mind the various characteristics of industrial process systems. Part 3 is focused on artificial neural networks and deep learning. Part 4 covers the important topic of deploying ML solutions over web and shows how to build a production-ready process monitoring web application. Broadly, the book covers the following: Varied applications of ML in process industry Fundamentals of machine learning workflow Practical methodologies for pre-processing industrial data Classical ML methods and their application for process monitoring, fault diagnosis, and soft sensing Deep learning and its application for predictive maintenance Reinforcement learning and its application for process control Deployment of ML solution over web
Control Charts And Machine Learning For Anomaly Detection In Manufacturing
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Author : Kim Phuc Tran
language : en
Publisher: Springer Nature
Release Date : 2021-08-29
Control Charts And Machine Learning For Anomaly Detection In Manufacturing written by Kim Phuc Tran and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-29 with Technology & Engineering categories.
This book introduces the latest research on advanced control charts and new machine learning approaches to detect abnormalities in the smart manufacturing process. By approaching anomaly detection using both statistics and machine learning, the book promotes interdisciplinary cooperation between the research communities, to jointly develop new anomaly detection approaches that are more suitable for the 4.0 Industrial Revolution. The book provides ready-to-use algorithms and parameter sheets, enabling readers to design advanced control charts and machine learning-based approaches for anomaly detection in manufacturing. Case studies are introduced in each chapter to help practitioners easily apply these tools to real-world manufacturing processes. The book is of interest to researchers, industrial experts, and postgraduate students in the fields of industrial engineering, automation, statistical learning, and manufacturing industries.
Advanced Systems For Biomedical Applications
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Author : Olfa Kanoun
language : en
Publisher: Springer Nature
Release Date : 2021-07-19
Advanced Systems For Biomedical Applications written by Olfa Kanoun and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-19 with Technology & Engineering categories.
The book highlights recent developments in the field of biomedical systems covering a wide range of technological aspects, methods, systems and instrumentation techniques for diagnosis, monitoring, treatment, and assistance. Biomedical systems are becoming increasingly important in medicine and in special areas of application such as supporting people with disabilities and under pandemic conditions. They provide a solid basis for supporting people and improving their health care. As such, the book offers a key reference guide about novel medical systems for students, engineers, designers, and technicians.