Control Charts and Machine Learning for An...

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Sprache: Englisch

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Beschreibung

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.

Über den Autor

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.

Zusammenfassung

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

Inhaltsverzeichnis

Anomaly Detection in Manufacturing.- EWMA Time-Between-Events-and-Amplitude Control Charts for Correlated Data.- An Adaptive Exponentially Weighted Moving Average Chart for the Ratio of Two Normal Variables.- On the Performance of CUSUM t Chart in the Presence of Measurement Errors.- The Effect of Autocorrelation on the Shewhart Control Chart for the Ratio of Two Normal Variables.- LSTM Autoencoder Control Chart for Multivariate Time Series Data.- Real-Time Production Monitoring Approach for Smart Manufacturing with Artificial Intelligence Techniques.- Anomaly Detection in Graph with Machine Learning.- Profile Control Charts Based on Support Vector Data Description.- An Anomaly Detection Approach Based on the Combination of LSTM Autoencoder and Isolation Forest for Multivariate Time Series Data.

Details

Erscheinungsjahr:	2021
Fachbereich:	Fertigungstechnik
Genre:	Technik
Rubrik:	Naturwissenschaften & Technik
Medium:	Buch
Seiten:	276
Reihe:	Springer Series in Reliability Engineering
Inhalt:	vi 269 S. 29 s/w Illustr. 38 farbige Illustr. 269 p. 67 illus. 38 illus. in color.
ISBN-13:	9783030838188
ISBN-10:	3030838188
Sprache:	Englisch
Ausstattung / Beilage:	HC runder Rücken kaschiert
Einband:	Gebunden
Redaktion:	Tran, Kim Phuc
Herausgeber:	Kim Phuc Tran
Auflage:	1st ed. 2022
Hersteller:	Springer International Publishing Springer International Publishing AG Springer Series in Reliability Engineering
Maße:	241 x 160 x 21 mm
Von/Mit:	Kim Phuc Tran
Erscheinungsdatum:	30.08.2021
Gewicht:	0,582 kg

preigu-id: 120318302