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Control Charts and Machine Learning for Anomaly Detection in Manufacturing
Editor
- Kim Phuc Tran(University of Lille)
Abstract
Individual chapters are listed in the "Chapters" tab
Suggested Citation
DOI: 10.1007/978-3-030-83819-5
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Book Chapters
The following chapters of this book are listed in IDEAS- Kim Phuc Tran, 2022. "Introduction to Control Charts and Machine Learning for Anomaly Detection in Manufacturing," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 1-6, Springer.
- Phuong Hanh Tran & Adel Ahmadi Nadi & Thi Hien Nguyen & Kim Duc Tran & Kim Phuc Tran, 2022. "Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 7-42, Springer.
- Philippe Castagliola & Giovanni Celano & Dorra Rahali & Shu Wu, 2022. "Control Charts for Monitoring Time-Between-Events-and-Amplitude Data," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 43-76, Springer.
- Maria Anastasopoulou & Athanasios C. Rakitzis, 2022. "Monitoring a BAR(1) Process with EWMA and DEWMA Control Charts," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 77-103, Springer.
- Christian H. Weiß, 2022. "On Approaches for Monitoring Categorical Event Series," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 105-129, Springer.
- Xiulin Xie & Peihua Qiu, 2022. "Machine Learning Control Charts for Monitoring Serially Correlated Data," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 131-147, Springer.
- Tommaso Barbariol & Filippo Dalla Chiara & Davide Marcato & Gian Antonio Susto, 2022. "A Review of Tree-Based Approaches for Anomaly Detection," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 149-185, Springer.
- Anne-Sophie Collin & Christophe Vleeschouwer, 2022. "Joint Use of Skip Connections and Synthetic Corruption for Anomaly Detection with Autoencoders," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 187-215, Springer.
- Edgard M. Maboudou-Tchao & Charles W. Harrison, 2022. "A Comparative Study of $$\text {L}_1$$ L 1 and $$\text {L}_2$$ L 2 Norms in Support Vector Data Descriptions," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 217-241, Springer.
- Khanh T. P. Nguyen, 2022. "Feature Engineering and Health Indicator Construction for Fault Detection and Diagnostic," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 243-269, Springer.
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