語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Dynamic Modeling of Complex Industri...
~
SpringerLink (Online service)
Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research/ by Chao Shang.
作者:
Shang, Chao.
面頁冊數:
XVIII, 143 p. 59 illus., 46 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Quality control. -
電子資源:
https://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 Processes: Data-driven Methods and Application Research
[electronic resource] /by Chao Shang. - 1st ed. 2018. - XVIII, 143 p. 59 illus., 46 illus. in color.online resource. - Springer Theses, Recognizing Outstanding Ph.D. Research,2190-5053. - Springer Theses, Recognizing Outstanding Ph.D. Research,.
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:
573723
Quality control.
LC Class. No.: TA169.7
Dewey Class. No.: 658.56
Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research
LDR
:03229nam a22004215i 4500
001
997857
003
DE-He213
005
20200706103542.0
007
cr nn 008mamaa
008
201225s2018 si | s |||| 0|eng d
020
$a
9789811066771
$9
978-981-10-6677-1
024
7
$a
10.1007/978-981-10-6677-1
$2
doi
035
$a
978-981-10-6677-1
050
4
$a
TA169.7
050
4
$a
T55-55.3
072
7
$a
TGPR
$2
bicssc
072
7
$a
TEC032000
$2
bisacsh
072
7
$a
TGPR
$2
thema
082
0 4
$a
658.56
$2
23
100
1
$a
Shang, Chao.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1289244
245
1 0
$a
Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research
$h
[electronic resource] /
$c
by Chao Shang.
250
$a
1st ed. 2018.
264
1
$a
Singapore :
$b
Springer Singapore :
$b
Imprint: Springer,
$c
2018.
300
$a
XVIII, 143 p. 59 illus., 46 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
Springer Theses, Recognizing Outstanding Ph.D. Research,
$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
Quality control.
$3
573723
650
0
$a
Reliability.
$3
573603
650
0
$a
Industrial safety.
$3
568114
650
0
$a
Manufactures.
$3
680602
650
0
$a
Control engineering.
$3
1249728
650
0
$a
Statistics .
$3
1253516
650
1 4
$a
Quality Control, Reliability, Safety and Risk.
$3
671184
650
2 4
$a
Manufacturing, Machines, Tools, Processes.
$3
1226012
650
2 4
$a
Control and Systems Theory.
$3
1211358
650
2 4
$a
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
$3
782247
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9789811066764
776
0 8
$i
Printed edition:
$z
9789811066788
776
0 8
$i
Printed edition:
$z
9789811338892
830
0
$a
Springer Theses, Recognizing Outstanding Ph.D. Research,
$x
2190-5053
$3
1253569
856
4 0
$u
https://doi.org/10.1007/978-981-10-6677-1
912
$a
ZDB-2-ENG
912
$a
ZDB-2-SXE
950
$a
Engineering (SpringerNature-11647)
950
$a
Engineering (R0) (SpringerNature-43712)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入