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Integrated projection and regression...
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Khan, Anakaorn.
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Integrated projection and regression models for monitoring multivariate autocorrelated cascade processes.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Integrated projection and regression models for monitoring multivariate autocorrelated cascade processes./
作者:
Khan, Anakaorn.
面頁冊數:
130 p.
附註:
Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
Contained By:
Dissertation Abstracts International76-01B(E).
標題:
Industrial engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3633606
ISBN:
9781321134513
Integrated projection and regression models for monitoring multivariate autocorrelated cascade processes.
Khan, Anakaorn.
Integrated projection and regression models for monitoring multivariate autocorrelated cascade processes.
- 130 p.
Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
Thesis (Ph.D.)--North Dakota State University, 2014.
This item must not be sold to any third party vendors.
This dissertation presents a comprehensive methodology of dual monitoring for the multivariate autocorrelated cascade processes using principal component analysis and regression. Principle Components Analysis is used to alleviate the multicollinearity among input process variables and reduce the dimension of the variables. An integrated principal components selection rule is proposed to reduce the number of input variables. An autoregressive time series model is used and imposed on the time correlated output variable which depends on many multicorrelated process input variables. A generalized least squares principal component regression is used to describe the relationship between product and process variables under the autoregressive regression error model. The combined residual based EWMA control chart, applied to the product characteristics, and the MEWMA control charts applied to the multivariate autocorrelated cascade process characteristics, are proposed.
ISBN: 9781321134513Subjects--Topical Terms:
526216
Industrial engineering.
Integrated projection and regression models for monitoring multivariate autocorrelated cascade processes.
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Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
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Adviser: Canan Bilen-Green.
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Thesis (Ph.D.)--North Dakota State University, 2014.
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This dissertation presents a comprehensive methodology of dual monitoring for the multivariate autocorrelated cascade processes using principal component analysis and regression. Principle Components Analysis is used to alleviate the multicollinearity among input process variables and reduce the dimension of the variables. An integrated principal components selection rule is proposed to reduce the number of input variables. An autoregressive time series model is used and imposed on the time correlated output variable which depends on many multicorrelated process input variables. A generalized least squares principal component regression is used to describe the relationship between product and process variables under the autoregressive regression error model. The combined residual based EWMA control chart, applied to the product characteristics, and the MEWMA control charts applied to the multivariate autocorrelated cascade process characteristics, are proposed.
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The dual EWMA and MEWMA control chart has advantage and capability over the conventional residual type control chart applied to the residuals of the principal component regression by monitoring both product and the process characteristics simultaneously. The EWMA control chart is used to increase the detection performance, especially in the case of small mean shifts. The MEWMA is applied to the selected set of variables from the first principal component with the aim of increasing the sensitivity in detecting process failures. The dual implementation control chart for product and process characteristics enhances both the detection and the prediction performance of the monitoring system of the multivariate autocorrelated cascade processes. The proposed methodology is demonstrated through an example of the sugar-beet pulp drying process. A general guideline for controlling multivariate autocorrelated processes is also developed.
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