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Improving monitoring and diagnosis f...

Portland State University.

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Improving monitoring and diagnosis for process control using independent component analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improving monitoring and diagnosis for process control using independent component analysis./
作者:	Shannon, Thaddeus Taylor, III.
面頁冊數:	134 p.
附註:	Source: Dissertation Abstracts International, Volume: 68-07, Section: B, page: 4799.
Contained By:	Dissertation Abstracts International68-07B.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3273614
ISBN:	9780549135685

Improving monitoring and diagnosis for process control using independent component analysis.
Shannon, Thaddeus Taylor, III.

Improving monitoring and diagnosis for process control using independent component analysis. - 134 p.

Source: Dissertation Abstracts International, Volume: 68-07, Section: B, page: 4799.

Thesis (Ph.D.)--Portland State University, 2007.

Statistical Process Control (SPC) is the general field concerned with monitoring the operation and performance of systems. SPC consists of a collection of techniques for characterizing the operation of a system using a probability distribution consistent with the system's inputs and outputs. Classical SPC monitors a single variable to characterize the operation of a single machine tool or process step using tools such as Shewart charts. The traditional approach works well for simple small to medium size processes. For more complex processes a number of multivariate SPC techniques have been developed in recent decades. These advanced methods suffer from several disadvantages compared to univariate techniques: they tend to be statistically less powerful, and they tend to complicate process diagnosis when a disturbance is detected.

ISBN: 9780549135685Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Improving monitoring and diagnosis for process control using independent component analysis.
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502 $a Thesis (Ph.D.)--Portland State University, 2007.
520 $a Statistical Process Control (SPC) is the general field concerned with monitoring the operation and performance of systems. SPC consists of a collection of techniques for characterizing the operation of a system using a probability distribution consistent with the system's inputs and outputs. Classical SPC monitors a single variable to characterize the operation of a single machine tool or process step using tools such as Shewart charts. The traditional approach works well for simple small to medium size processes. For more complex processes a number of multivariate SPC techniques have been developed in recent decades. These advanced methods suffer from several disadvantages compared to univariate techniques: they tend to be statistically less powerful, and they tend to complicate process diagnosis when a disturbance is detected.
520 $a This research introduces a general method for simplifying multivariate process monitoring in such a manner as to allow the use of traditional SPC tools while facilitating process diagnosis. Latent variable representations of complex processes are developed which directly relate disturbances with process steps or segments. The method models disturbances in the process rather than the process itself. The basic tool used is Independent Component Analysis (ICA). The methodology is illustrated on the problem of monitoring Electrical Test (E-Test) data from a semiconductor manufacturing process. Development and production data from a working semiconductor plant are used to estimate a factor model that is then used to develop univariate control charts for particular types of process disturbances. Detection and false alarm rates for data with known disturbances are given. The charts correctly detect and classify all the disturbance cases with a very low false alarm rate.
520 $a A secondary contribution is the introduction of a method for performing an ICA like analysis using possibilistic data instead of probabilistic data. This technique extends the general ICA framework to apply to a broader range of uncertainty types. Further development of this technique could lead to the capability to use extremely sparse data to estimate ICA process models.
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