Control Charts and Machine Learning for Anomaly Detection in Manufacturing

✍ Scribed by Kim Phuc Tran

Publisher: Springer
Year: 2021
Tongue: English
Leaves: 275
Series: Springer Series in Reliability Engineering
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

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