Control Charts and Machine Learning for Anomaly Detection in Manufacturing
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Beschreibung
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.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
Autor*in
Kim Phuc Tran
Themen in »Control Charts and Machine Learning for Anomaly Detection in Manufacturing«
Control Charts Anomaly Detection Statistical Quality Control One-class Classification Statistical Process Monitoring Data Mining Smart Manufacturing Manufacturing Processes Failure Prediction