Xiangyu Kong, Jiayu Luo, Xiaowei Feng

Process Monitoring and Fault Diagnosis Based on Multivariable Statistical Analysis (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book reports recent developments of the multivariate statistical process control (MSPC) methods for industrial process monitoring and fault diagnosis. Specifically, this book gives an overview of recently developed methods in different aspects, namely system-wide process monitoring, quality-related time-varying process monitoring, quality-related dynamic process monitoring, quality-related complex nonlinear process monitoring, and quality-related fault subspace extraction for fault diagnosis, non-Gaussian process monitoring and fault diagnosis, etc. In order to help readers understand and master the new methods, before presenting each new method in each chapter, a specialized section is provided to review the closely related several basis models. Throughout the book, detailed steps of new methods are provided, and all new algorithms or methods proposed by us are tested and verified by numerical simulations or Tennessee Eastman benchmark chemical process. Readers find illustrative demonstration examples on a range of industrial processes to study and the present deficiency and recent developments of the MSPC methods for industrial processes monitoring and fault diagnosis, by learning from the authors' latest achievements or new methods around the practical industrial needs. This book is assimilated by advanced undergraduates and graduate students, as well as industrial and process engineering researchers and practitioners.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 300
• Erscheinungstermin: 12. März 2024
• Englisch
• ISBN-13: 9789819987757
• Artikelnr.: 70172173

Autorenporträt

Xiangyu Kong received the B.S. degree in optical engineering from Beijing Institute of Technology, P. R. China, in 1990, the M.S. degree in mechanical and electrical engineering from Xi'an Institute of Hi-Tech, in 2000, and the Ph.D. degree in automation science and engineering from Xi'an Jiaotong University, P. R. China, in 2005. He is currently a professor in the Department of Control Engineering of Xi'an Institute of Hi-Tech. His research interests include adaptive signal processing, neural networks and feature extraction, process monitoring, and fault diagnosis. He has published six monographs (all first author), including an English monograph published by Springer, and more than 150 papers, in which more than 40 articles were published in premier journals including IEEE Transactions on Signal Processing, IEEE Transactions on Neural Networks and Learning Systems, and Neural Networks. He has been PIs of four grants from the National Natural Science Foundation of China.

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.

Inhaltsangabe

Chapter 1 Introduction.- Chapter 2 An Overview of Conventional MSPC Methods.- Chapter 3 System-wide Process Monitoring and Fault Diagnosis.- Chapter 4 Quality-Related Time-Varying Process Monitoring.- Chapter 5 Quality-Related Dynamic Process Monitoring: Part I.- Chapter 6 Quality-Related Dynamic Process Monitoring: Part II.- Chapter 7 Quality-Related Complex Nonlinear Process Monitoring.- Chapter 8 Quality-Related Fault Subspace Extraction for Fault Diagnosis.- Chapter 9 Non-Gaussian Process Monitoring and Fault Diagnosis.- Chapter 10 Hybrid Gaussian/Non-Gaussian Quality-Related Nonlinear Process Monitoring.- Chapter 11 Conclusions and Future Work.