國立高雄大學圖資館 |

Language: English

Back

Process monitoring and fault diagnosis based on multivariable statistical analysis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Process monitoring and fault diagnosis based on multivariable statistical analysisby Xiangyu Kong, Jiayu Luo, Xiaowei Feng.
Author:	Kong, Xiangyu.
other author:	Luo, Jiayu.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	xxxiii, 300 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Process controlStatistical methods.
Online resource:	https://doi.org/10.1007/978-981-99-8775-7
ISBN:	9789819987757$q(electronic bk.)

Process monitoring and fault diagnosis based on multivariable statistical analysis
Kong, Xiangyu.

Process monitoring and fault diagnosis based on multivariable statistical analysis[electronic resource] /by Xiangyu Kong, Jiayu Luo, Xiaowei Feng. - Singapore :Springer Nature Singapore :2024. - xxxiii, 300 p. :ill. (chiefly col.), digital ;24 cm. - Engineering applications of computational methods,v. 192662-3374 ;. - Engineering applications of computational methods ;v.2..

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.

ISBN: 9789819987757$q(electronic bk.)

Standard No.: 10.1007/978-981-99-8775-7doiSubjects--Topical Terms:

181914
Process control
--Statistical methods.

LC Class. No.: TS156.8 / .K66 2024

Dewey Class. No.: 658.5620151

Process monitoring and fault diagnosis based on multivariable statistical analysis
LDR:03323nmm a22003615a 4500 001 665313
003 DE-He213
005 20240312182124.0
006 m d
007 cr nn 008maaau
008 241219s2024 si s 0 eng d
020 $a 9789819987757$q(electronic bk.)
020 $a 9789819987740$q(paper)
024 7 $a 10.1007/978-981-99-8775-7 $2 doi
035 $a 978-981-99-8775-7
040 $a GP $c GP
041 0 $a eng
050 4 $a TS156.8 $b .K66 2024
072 7 $a PBWH $2 bicssc
072 7 $a TBJ $2 bicssc
072 7 $a MAT003000 $2 bisacsh
072 7 $a PBWH $2 thema
072 7 $a TBJ $2 thema
082 0 4 $a 658.5620151 $2 23
090 $a TS156.8 $b .K82 2024
100 1 $a Kong, Xiangyu. $3 773111
245 1 0 $a Process monitoring and fault diagnosis based on multivariable statistical analysis $h [electronic resource] / $c by Xiangyu Kong, Jiayu Luo, Xiaowei Feng.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xxxiii, 300 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a Engineering applications of computational methods, $x 2662-3374 ; $v v. 19
505 0 $a 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.
520 $a 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.
650 0 $a Process control $x Statistical methods. $3 181914
650 1 4 $a Mathematical Modeling and Industrial Mathematics. $3 274070
650 2 4 $a Industrial Management. $3 910688
650 2 4 $a Control and Systems Theory. $3 825946
700 1 $a Luo, Jiayu. $3 978117
700 1 $a Feng, Xiaowei. $3 978118
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Engineering applications of computational methods ; $v v.2. $3 869168
856 4 0 $u https://doi.org/10.1007/978-981-99-8775-7
950 $a Mathematics and Statistics (SpringerNature-11649)