Control Charts and Machine Learning for Anomaly Detection in Manufacturing

Edited by Kim Phuc Tran ()

in Springer Series in Reliability Engineering from Springer, currently edited by Hoang Pham

Date: 2022
ISBN: 978-3-030-83819-5
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Chapters in this book:

Introduction to Control Charts and Machine Learning for Anomaly Detection in Manufacturing

Kim Phuc Tran

Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective

Phuong Hanh Tran, Adel Ahmadi Nadi, Thi Hien Nguyen, Kim Duc Tran and Kim Phuc Tran

Control Charts for Monitoring Time-Between-Events-and-Amplitude Data

Philippe Castagliola, Giovanni Celano, Dorra Rahali and Shu Wu

Monitoring a BAR(1) Process with EWMA and DEWMA Control Charts

Maria Anastasopoulou and Athanasios C. Rakitzis

On Approaches for Monitoring Categorical Event Series

Christian H. Weiß

Machine Learning Control Charts for Monitoring Serially Correlated Data

Xiulin Xie and Peihua Qiu

A Review of Tree-Based Approaches for Anomaly Detection

Tommaso Barbariol, Filippo Dalla Chiara, Davide Marcato and Gian Antonio Susto

Joint Use of Skip Connections and Synthetic Corruption for Anomaly Detection with Autoencoders

Anne-Sophie Collin and Christophe Vleeschouwer

A Comparative Study of $$\text {L}_1$$ L 1 and $$\text {L}_2$$ L 2 Norms in Support Vector Data Descriptions

Edgard M. Maboudou-Tchao and Charles W. Harrison

Feature Engineering and Health Indicator Construction for Fault Detection and Diagnostic

Khanh T. P. Nguyen

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DOI: 10.1007/978-3-030-83819-5

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