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Control charts and machine learning for anomaly detection in manufacturing

Record Type:	Electronic resources : Monograph/item
Title/Author:	Control charts and machine learning for anomaly detection in manufacturingedited by Kim Phuc Tran.
other author:	Tran, Kim Phuc.
Published:	Cham :Springer International Publishing :2022.
Description:	vi, 269 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-030-83819-5
ISBN:	9783030838195$q(electronic bk.)

Control charts and machine learning for anomaly detection in manufacturing
Control charts and machine learning for anomaly detection in manufacturing[electronic resource] /edited by Kim Phuc Tran. - Cham :Springer International Publishing :2022. - vi, 269 p. :ill. (some col.), digital ;24 cm. - Springer series in reliability engineering,2196-999X. - Springer series in reliability engineering..

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.

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.

ISBN: 9783030838195$q(electronic bk.)

Standard No.: 10.1007/978-3-030-83819-5doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .C65 2022

Dewey Class. No.: 006.31

Control charts and machine learning for anomaly detection in manufacturing
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245 0 0 $a Control charts and machine learning for anomaly detection in manufacturing $h [electronic resource] / $c edited by Kim Phuc Tran.
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505 0 $a 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.
520 $a 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.
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650 2 4 $a Engineering Economics, Organization, Logistics, Marketing. $3 274009
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Applied Statistics. $3 805583
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700 1 $a Tran, Kim Phuc. $3 917425
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