Control Charts and Machine Learning for Anomaly Detection in Manufacturing

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.

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

Autorentext
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.

Inhalt
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.<p