Control Charts and Machine Learning for Anomaly Detection in Manufacturing

Kim Phuc Tran

ISBN 10: 3030838218 / ISBN 13: 9783030838218

Published by Springer, Palgrave Macmillan Aug 2022, 2022

About this Item

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 # 9783030838218

Bibliographic Details

• Title: Control Charts and Machine Learning for Anomaly Detection in Manufacturing
• Publisher: Springer, Palgrave Macmillan Aug 2022
• Publication Date: 2022
• Language: English
• ISBN 10: 3030838218
• ISBN 13: 9783030838218
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 16D millimeters 235 millimeters width by 155 millimeters height by 16 millimeters depth
• Weight: 423 grams

About this title

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.

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.