東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Dynamic modeling of complex industri...

Shang, Chao.

Linked to FindBook

Google Book

Amazon

博客來

Dynamic modeling of complex industrial processes = data-driven methods and application research /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dynamic modeling of complex industrial processes/ by Chao Shang.
Reminder of title:	data-driven methods and application research /
Author:	Shang, Chao.
Published:	Singapore :Springer Singapore : : 2018.,
Description:	xviii, 143 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Concurrent monitoring of steady state and process dynamics with SFA -- Online monitoring and diagnosis of control performance with SFA and contribution plots -- Recursive SFA algorithm and adaptive monitoring system design -- Probabilistic SFR model and its applications in dynamic quality prediction -- Improved DPLS model with temporal smoothness and its applications in dynamic quality prediction -- Nonlinear and dynamic soft sensing model based on Bayesian framework -- Summary and open problems.
Contained By:	Springer eBooks
Subject:	Manufacturing processes - Mathematical models. -
Online resource:	http://dx.doi.org/10.1007/978-981-10-6677-1
ISBN:	9789811066771

Dynamic modeling of complex industrial processes = data-driven methods and application research /
Shang, Chao.

Dynamic modeling of complex industrial processesdata-driven methods and application research /[electronic resource] :by Chao Shang. - Singapore :Springer Singapore :2018. - xviii, 143 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

Introduction -- Concurrent monitoring of steady state and process dynamics with SFA -- Online monitoring and diagnosis of control performance with SFA and contribution plots -- Recursive SFA algorithm and adaptive monitoring system design -- Probabilistic SFR model and its applications in dynamic quality prediction -- Improved DPLS model with temporal smoothness and its applications in dynamic quality prediction -- Nonlinear and dynamic soft sensing model based on Bayesian framework -- Summary and open problems.

This thesis develops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principle. Using said framework as a point of departure, it then proposes novel strategies for dealing with control monitoring and quality prediction problems in industrial production contexts. The thesis reveals the slowly varying nature of industrial production processes under feedback control, and integrates it with process data analytics to offer powerful prior knowledge that gives rise to statistical methods tailored to industrial data. It addresses several issues of immediate interest in industrial practice, including process monitoring, control performance assessment and diagnosis, monitoring system design, and product quality prediction. In particular, it proposes a holistic and pragmatic design framework for industrial monitoring systems, which delivers effective elimination of false alarms, as well as intelligent self-running by fully utilizing the information underlying the data. One of the strengths of this thesis is its integration of insights from statistics, machine learning, control theory and engineering to provide a new scheme for industrial process modeling in the era of big data.

ISBN: 9789811066771

Standard No.: 10.1007/978-981-10-6677-1doiSubjects--Topical Terms:

920932
Manufacturing processes
--Mathematical models.

LC Class. No.: TS183

Dewey Class. No.: 670.15118

Dynamic modeling of complex industrial processes = data-driven methods and application research /
LDR:02769nmm a2200325 a 4500 001 2133970
003 DE-He213
005 20180222151139.0
006 m d
007 cr nn 008maaau
008 181005s2018 si s 0 eng d
020 $a 9789811066771 $q (electronic bk.)
020 $a 9789811066764 $q (paper)
024 7 $a 10.1007/978-981-10-6677-1 $2 doi
035 $a 978-981-10-6677-1
040 $a GP $c GP
041 0 $a eng
050 4 $a TS183
072 7 $a TGPR $2 bicssc
072 7 $a TEC032000 $2 bisacsh
082 0 4 $a 670.15118 $2 23
090 $a TS183 $b .S528 2018
100 1 $a Shang, Chao. $3 3301970
245 1 0 $a Dynamic modeling of complex industrial processes $h [electronic resource] : $b data-driven methods and application research / $c by Chao Shang.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2018.
300 $a xviii, 143 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5053
505 0 $a Introduction -- Concurrent monitoring of steady state and process dynamics with SFA -- Online monitoring and diagnosis of control performance with SFA and contribution plots -- Recursive SFA algorithm and adaptive monitoring system design -- Probabilistic SFR model and its applications in dynamic quality prediction -- Improved DPLS model with temporal smoothness and its applications in dynamic quality prediction -- Nonlinear and dynamic soft sensing model based on Bayesian framework -- Summary and open problems.
520 $a This thesis develops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principle. Using said framework as a point of departure, it then proposes novel strategies for dealing with control monitoring and quality prediction problems in industrial production contexts. The thesis reveals the slowly varying nature of industrial production processes under feedback control, and integrates it with process data analytics to offer powerful prior knowledge that gives rise to statistical methods tailored to industrial data. It addresses several issues of immediate interest in industrial practice, including process monitoring, control performance assessment and diagnosis, monitoring system design, and product quality prediction. In particular, it proposes a holistic and pragmatic design framework for industrial monitoring systems, which delivers effective elimination of false alarms, as well as intelligent self-running by fully utilizing the information underlying the data. One of the strengths of this thesis is its integration of insights from statistics, machine learning, control theory and engineering to provide a new scheme for industrial process modeling in the era of big data.
650 0 $a Manufacturing processes $x Mathematical models. $3 920932
650 0 $a Manufacturing processes $x Computer simulation. $3 841343
650 0 $a Manufacturing processes $x Data processing. $3 654415
650 1 4 $a Engineering. $3 586835
650 2 4 $a Quality Control, Reliability, Safety and Risk. $3 891027
650 2 4 $a Manufacturing, Machines, Tools. $3 891025
650 2 4 $a Control. $3 1006321
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 1005896
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Springer theses. $3 1314442
856 4 0 $u http://dx.doi.org/10.1007/978-981-10-6677-1
950 $a Engineering (Springer-11647)