Author: Ankur Kumar
Publisher: MLforPSE
ISBN:
Category : Computers
Languages : en
Pages : 354
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Book Description
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
Author: Ankur Kumar
Publisher: MLforPSE
ISBN:
Category : Computers
Languages : en
Pages : 208
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Book Description
This book is designed to help readers gain a working-level knowledge of machine learning-based dynamic process modeling techniques that have proven useful in process industry. Readers can leverage the concepts learned to build advanced solutions for process monitoring, soft sensing, inferential modeling, predictive maintenance, and process control for dynamic systems. The application-focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers, and data scientists. 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 for dynamic process modeling. Upon completion, readers will be able to confidently navigate the system identification literature and make judicious selection of modeling approaches suitable for their problems. This book has been divided into three parts. Part 1 of the book provides perspectives on the importance of ML for dynamic process modeling and lays down the basic foundations of ML-DPM (machine learning for dynamic process modeling). Part 2 provides in-detail presentation of classical ML techniques and has been written keeping in mind the different modeling requirements and process characteristics that determine a model’s suitability for a problem at hand. These include, amongst others, presence of multiple correlated outputs, process nonlinearity, need for low model bias, need to model disturbance signal accurately, etc. Part 3 is focused on artificial neural networks and deep learning. The following topics are broadly covered: · Exploratory analysis of dynamic dataset · Best practices for dynamic modeling · Linear and discrete-time classical parametric and non-parametric models · State-space models for MIMO systems · Nonlinear system identification and closed-loop identification · Neural networks-based dynamic process modeling
Author: Ankur Kumar
Publisher: MLforPSE
ISBN:
Category : Computers
Languages : en
Pages : 365
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Book Description
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
Author: Ankur Kumar
Publisher: MLforPSE
ISBN:
Category : Computers
Languages : en
Pages : 69
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Book Description
This book is designed to help readers gain quick familiarity with deep learning-based computer vision and abnormal equipment sound detection techniques. The book helps you take your first step towards learning how to use convolutional neural networks (the ANN architecture that is behind the modern revolution in computer vision) and build image sensor-based manufacturing defect detection solutions. A quick introduction is also provided to how modern predictive maintenance solutions can be built for process critical equipment by analyzing the sound generated by the equipment. Overall, this short eBook sets the foundation with which budding process data scientists can confidently navigate the world of modern computer vision and acoustic monitoring.
Author: Jingzheng Ren
Publisher: Elsevier
ISBN: 012821743X
Category : Technology & Engineering
Languages : en
Pages : 542
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Book Description
Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis Gives direction to future development trends of AI technologies in chemical and process engineering
Author: Yoshiyuki Yamashita
Publisher: Elsevier
ISBN: 0323853668
Category : Technology & Engineering
Languages : en
Pages : 2304
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Book Description
14th International Symposium on Process Systems Engineering, Volume 49 brings together the international community of researchers and engineers interested in computing-based methods in process engineering. The conference highlights the contributions of the PSE community towards the sustainability of modern society and is based on the 2021 event held in Tokyo, Japan, July 1-23, 2021. It contains contributions from academia and industry, establishing the core products of PSE, defining the new and changing scope of our results, and covering future challenges. Plenary and keynote lectures discuss real-world challenges (globalization, energy, environment and health) and contribute to discussions on the widening scope of PSE versus the consolidation of the core topics of PSE. Highlights how the Process Systems Engineering community contributes to the sustainability of modern society Establishes the core products of Process Systems Engineering Defines the future challenges of Process Systems Engineering
Author: Sio-Iong Ao
Publisher: Springer Science & Business Media
ISBN: 9048194199
Category : Technology & Engineering
Languages : en
Pages : 607
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Book Description
A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). Machine Learning and Systems Engineering contains forty-six revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Machine Learning and Systems Engineering offers the state of the art of tremendous advances in machine learning and systems engineering and also serves as an excellent reference text for researchers and graduate students, working on machine learning and systems engineering.
Author: Dipanjan Sarkar
Publisher: Apress
ISBN: 1484232070
Category : Computers
Languages : en
Pages : 545
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Book Description
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students
Author: Mario R. Eden
Publisher: Elsevier
ISBN: 0444642420
Category : Technology & Engineering
Languages : en
Pages : 2602
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Book Description
Process Systems Engineering brings together the international community of researchers and engineers interested in computing-based methods in process engineering. This conference highlights the contributions of the PSE community towards the sustainability of modern society and is based on the 13th International Symposium on Process Systems Engineering PSE 2018 event held San Diego, CA, July 1-5 2018. The book contains contributions from academia and industry, establishing the core products of PSE, defining the new and changing scope of our results, and future challenges. Plenary and keynote lectures discuss real-world challenges (globalization, energy, environment and health) and contribute to discussions on the widening scope of PSE versus the consolidation of the core topics of PSE. Highlights how the Process Systems Engineering community contributes to the sustainability of modern society Establishes the core products of Process Systems Engineering Defines the future challenges of Process Systems Engineering
Author: Gintaras V. Reklaitis
Publisher: Springer Science & Business Media
ISBN: 3642609724
Category : Science
Languages : en
Pages : 886
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Book Description
Batch chemical processing has in the past decade enjoyed a return to respectability as a valuable, effective, and often preferred mode of process operation. This book provides the first comprehensive and authoritative coverage that reviews the state of the art development in the field of batch chemical systems engineering, applications in various chemical industries, current practice in different parts of the world, and future technical challenges. Developments in enabling computing technologies such as simulation, mathematical programming, knowledge based systems, and prognosis of how these developments would impact future progress in the batch domain are covered. Design issues for complex unit processes and batch plants as well as operational issues such as control and scheduling are also addressed.
