Overview
- Presents an interdisciplinary approach to detect anomalies in smart manufacturing processes
- Explains both advanced control charts and machine learning approaches
- Offers ready-to-use algorithms, parameter sheets, and numerous case studies
Part of the book series: Springer Series in Reliability Engineering (RELIABILITY)
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Table of contents (10 chapters)
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Front Matter
Keywords
About this book
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.Editors and Affiliations
About the editor
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.
Bibliographic Information
Book Title: Control Charts and Machine Learning for Anomaly Detection in Manufacturing
Editors: Kim Phuc Tran
Series Title: Springer Series in Reliability Engineering
DOI: https://doi.org/10.1007/978-3-030-83819-5
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-83818-8Published: 30 August 2021
Softcover ISBN: 978-3-030-83821-8Published: 31 August 2022
eBook ISBN: 978-3-030-83819-5Published: 29 August 2021
Series ISSN: 1614-7839
Series E-ISSN: 2196-999X
Edition Number: 1
Number of Pages: VI, 269
Number of Illustrations: 29 b/w illustrations, 38 illustrations in colour
Topics: Manufacturing, Machines, Tools, Processes, Engineering Economics, Organization, Logistics, Marketing, Machine Learning, Applied Statistics, Cyber-physical systems, IoT