Control Charts and Machine Learning for Anomaly Detection in Manufacturing



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Éditeur :

Springer


Paru le : 2021-08-29



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Description

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.

Pages
269 pages
Collection
n.c
Parution
2021-08-29
Marque
Springer
EAN papier
9783030838188
EAN PDF
9783030838195

Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
26
Taille du fichier
7014 Ko
Prix
160,49 €
EAN EPUB
9783030838195

Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
26
Taille du fichier
30915 Ko
Prix
160,49 €

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.

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