Control Charts and Machine Learning for Anomaly Detection in Manufacturing
Springer International Publishing (Verlag)
978-3-030-83818-8 (ISBN)
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.lt;p>Dr. Kim Phuc Tran is an associate professor at ENSAIT University and GEMTEX Laboratory, France. His research is focused on anomaly detection and machine learning with applications. He has published more than 30 papers in peer-reviewed international journals.
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.
| Erscheinungsdatum | 31.08.2021 |
|---|---|
| Reihe/Serie | Springer Series in Reliability Engineering |
| Zusatzinfo | VI, 269 p. 67 illus., 38 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 537 g |
| Themenwelt | Technik ► Maschinenbau |
| Schlagworte | Anomaly Detection • control charts • Data Mining • failure prediction • machine learning • Manufacturing Processes • one-class classification • smart manufacturing • Statistical Process Monitoring • Statistical Quality Control |
| ISBN-10 | 3-030-83818-8 / 3030838188 |
| ISBN-13 | 978-3-030-83818-8 / 9783030838188 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
