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Control Charts and Machine Learning for Anomaly Detection in Manufacturing (Springer Series in Reliability Engineering) - Hardcover
Book 72 of 90: Springer Series in Reliability Engineering
Synopsis
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."synopsis" may belong to another edition of this title.
About the Author
Dr. Kim Phuc Tran is an Associate Professor of Artificial Intelligence and Data Science at the ENSAIT and the GEMTEX laboratory, University of Lille, France. His research focuses on anomaly detection and applications, decision support systems with artificial intelligence, federated learning, edge computing and applications. He has published more than 44 papers in international refereed journal papers, 5 book chapters, and 2 editorials as well as over 20 papers in conference proceedings.
From the Back Cover
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."About this title" may belong to another edition of this title.
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Control Charts and Machine Learning for Anomaly Detection in Manufacturing (Springer Series in Reliability Engineering)
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Control Charts and Machine Learning for Anomaly Detection in Manufacturing
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 prom. Seller Inventory # 485152240
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Control Charts and Machine Learning for Anomaly Detection in Manufacturing
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 276 pp. Englisch. Seller Inventory # 9783030838188
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Control Charts and Machine Learning for Anomaly Detection in Manufacturing (Springer Series in Reliability Engineering)
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Control Charts and Machine Learning for Anomaly Detection in Manufacturing
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 276 pp. Englisch. Seller Inventory # 9783030838188
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Control Charts and Machine Learning for Anomaly Detection in Manufacturing
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783030838188
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