Overview
- Focuses on the quality-related process monitoring of different situations, with MSPC-based models such as PCA, PLS
- Reviews the basic model and derives novel method with detailed steps
- Provides detailed formula derivation and solid experiment validation
Part of the book series: Engineering Applications of Computational Methods (EACM, volume 19)
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Table of contents (11 chapters)
Keywords
- MSPC-based methods
- Process monitoring
- Fault diagnosis
- Quality-related time-varying process monitoring
- Quality-related dynamic process monitoring
- Quality-related complex nonlinear process monitoring
- Reconstruction-based fault diagnosis
- Non-Gaussian process monitoring
- PCA
- PLS
- ICA
- Multivariate Statistical Process Control
About this book
Authors and Affiliations
About the authors
Jiayu Luo received Bachelor's degree from Hunan University, Hunan, China, in 2017, and Master's degree from Xi’an Institute of Hi-Tech., Xi’an, China, in 2019. He is currently pursuing Ph.D. degree in Xi’an Institute of Hi-Tech. He has published one book and 11 articles in IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Instrumentation and Measurement, IEEE Transactions on Signal Processing, and other journals. His research interests include feature extraction, complex process monitoring, and fault diagnosis.
Xiaowei Feng received his Bachelor's, Master's, and Ph.D. degrees from Xi'an Institute of High Tech., Xi'an, in 2008, 2011, and 2016, respectively. Now, he is working as a lecturer at Xi’an Institute of Hi-Tech. He has authored or co-authored more than 20 journal papers on IEEE Transactions on Signal Processing, IEEE Transactions on Neural Networks and Learning Systems,and other journals and has published 1 monograph. His research interests include feature extraction, industrial process monitoring, and fault diagnosis.
Bibliographic Information
Book Title: Process Monitoring and Fault Diagnosis Based on Multivariable Statistical Analysis
Authors: Xiangyu Kong, Jiayu Luo, Xiaowei Feng
Series Title: Engineering Applications of Computational Methods
DOI: https://doi.org/10.1007/978-981-99-8775-7
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Science Press 2024
Hardcover ISBN: 978-981-99-8774-0Published: 13 March 2024
Softcover ISBN: 978-981-99-8777-1Due: 13 April 2024
eBook ISBN: 978-981-99-8775-7Published: 12 March 2024
Series ISSN: 2662-3366
Series E-ISSN: 2662-3374
Edition Number: 1
Number of Pages: XXXIII, 300
Number of Illustrations: 7 b/w illustrations, 131 illustrations in colour
Topics: Mathematical Modeling and Industrial Mathematics, Engineering Economics, Organization, Logistics, Marketing, Control and Systems Theory