Control charts and machine learning for anomaly detection in manufacturing / Kim Phuc Tran, editors.
Tran, Kim Phuc, editor
2022
Q325.5
Available Online

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Title Control charts and machine learning for anomaly detection in manufacturing / Kim Phuc Tran, editors.
ISBN 9783030838195 (electronic bk.)
3030838196 (electronic bk.)
9783030838188 (print)
DOI https://doi.org/10.1007/978-3-030-83819-5
Published Cham, Switzerland : Springer, [2022]
Language English
Description 1 online resource (vi, 269 pages) : illustrations (some color)
Other Standard Identifiers 10.1007/978-3-030-83819-5 doi
Call Number Q325.5
Dewey Decimal Classification 006.3/1
Summary 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.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed August 31, 2021).
Added Author Tran, Kim Phuc, editor
Series Springer series in reliability engineering, 2196-999X
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
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.

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