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Control charts and machine learning ...

Tran, Kim Phuc.

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Control charts and machine learning for anomaly detection in manufacturing

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Control charts and machine learning for anomaly detection in manufacturing/ edited by Kim Phuc Tran.
其他作者:	Tran, Kim Phuc.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	vi, 269 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-030-83819-5
ISBN:	9783030838195

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

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

533906
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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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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