04468nam a22005895i 4500 978-981-99-8775-7 DE-He213 20251030111717.0 cr nn 008mamaa 240312s2024 si | s |||| 0|eng d 9789819987757 978-981-99-8775-7 10.1007/978-981-99-8775-7 doi TA342-343 PBWH bicssc TBJ bicssc MAT003000 bisacsh PBWH thema TBJ thema Kong, Xiangyu. author. aut http://id.loc.gov/vocabulary/relators/aut 72888 Process Monitoring and Fault Diagnosis Based on Multivariable Statistical Analysis [electronic resource] / by Xiangyu Kong, Jiayu Luo, Xiaowei Feng. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XXXIII, 300 p. 138 illus., 131 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Engineering Applications of Computational Methods, 2662-3374 ; 19 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. 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. Mathematical models. 66910 Industrial Management. 58197 Control engineering. Mathematical Modeling and Industrial Mathematics. 66912 Industrial Management. 58197 Control and Systems Theory. 65868 Luo, Jiayu. author. aut http://id.loc.gov/vocabulary/relators/aut 72889 Feng, Xiaowei. author. aut http://id.loc.gov/vocabulary/relators/aut 72890 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819987740 Printed edition: 9789819987764 Printed edition: 9789819987771 Engineering Applications of Computational Methods, 2662-3374 ; 19 72891 https://doi.org/10.1007/978-981-99-8775-7 ZDB-2-SMA ZDB-2-SXMS 58273 58273