Control Charts and Machine Learning for Anomaly Detection in Manufacturing
Control Charts and Machine Learning for Anomaly Detection in Manufacturing
edited by Kim Phuc Tran
- 1st edition 2022
- 1 recurso en línea (VI, 269 páginas) 67 ilustraciones, 38 ilustraciones a color
- Springer Series in Reliability Engineering 2196-999X .
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.
9783030838195
10.1007/978-3-030-83819-5 doi
Aprendizaje automático
Inteligencia artificial--Aplicaciones industriales
Q325.5 / 2022 EB
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.
9783030838195
10.1007/978-3-030-83819-5 doi
Aprendizaje automático
Inteligencia artificial--Aplicaciones industriales
Q325.5 / 2022 EB