東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

Innovative techniques for industrial...

He, Qinghua.

FindBook

Google Book

Amazon

博客來

Innovative techniques for industrial process modeling and monitoring.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Innovative techniques for industrial process modeling and monitoring./
作者:	He, Qinghua.
面頁冊數:	209 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2704.
Contained By:	Dissertation Abstracts International66-05B.
標題:	Engineering, Chemical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3174458
ISBN:	0542123371

Innovative techniques for industrial process modeling and monitoring.
He, Qinghua.

Innovative techniques for industrial process modeling and monitoring. - 209 p.

Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2704.

Thesis (Ph.D.)--The University of Texas at Austin, 2005.

This research presents several innovations in industrial process modeling and monitoring with the purpose of better controlling the process.

ISBN: 0542123371Subjects--Topical Terms:

1018531
Engineering, Chemical.

Innovative techniques for industrial process modeling and monitoring.
LDR:02922nmm 2200325 4500 001 1814893
005 20060719122842.5
008 130610s2005 eng d
020 $a 0542123371
035 $a (UnM)AAI3174458
035 $a AAI3174458
040 $a UnM $c UnM
100 1 $a He, Qinghua. $3 1904336
245 1 0 $a Innovative techniques for industrial process modeling and monitoring.
300 $a 209 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2704.
500 $a Supervisor: Sizhao Joe Qin.
502 $a Thesis (Ph.D.)--The University of Texas at Austin, 2005.
520 $a This research presents several innovations in industrial process modeling and monitoring with the purpose of better controlling the process.
520 $a In semiconductor manufacturing industry, people show increased interested in thermal modeling of Low-Pressure Chemical Vapor Deposition (LPCVD) processes in order to understand the process better and get tighter control of film uniformity. In this dissertation, a first principles transformed linear model is developed for the LPCVD process to address drawbacks of existing thermal models and facilitate, its industrial implementation.
520 $a In this work, a new valve suction model is proposed with simple structure and straightforward logic which make it easy to implement. Furthermore, several published valve suction detection techniques are reviewed. The inconsistency of Horch's first method is theoretically analyzed and illustrated by a simulated example. A new valve suction detection method is proposed based on curve-fitting for both self-regulating and integrating processes. The new method shows superior performance to other existing methods.
520 $a In this work, a new fault diagnosis method using fault directions in Fisher Discriminant Analysis (FDA) is developed in attempt to provide a better solution than the traditional contribution plot based on PCA. Besides, a new process monitoring method is proposed which consists of data pre-analysis, fault visualization and fault diagnosis.
520 $a In today's chemical industry, massive amount of data are easily available in computer controlled processes. But at the same time, the visualization of high dimensional data has been difficult. A general framework of hierarchical visualization is proposed and several multivariate visualization methods are developed in this work. The performance of PCA, PLS, Class Preserving Projection (CPP), FDA and two proposed approaches based on Support Vector Machines (SVM) are compared using an industrial data set. (Abstract shortened by UMI.)
590 $a School code: 0227.
650 4 $a Engineering, Chemical. $3 1018531
650 4 $a Engineering, Industrial. $3 626639
690 $a 0542
690 $a 0546
710 2 0 $a The University of Texas at Austin. $3 718984
773 0 $t Dissertation Abstracts International $g 66-05B.
790 1 0 $a Qin, Sizhao Joe, $e advisor
790 $a 0227
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3174458