کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1181579 | 962960 | 2010 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A unified statistical framework for monitoring multivariate systems with unknown source and error signals
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
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چکیده انگلیسی
This article proposes a unified multivariate statistical monitoring framework that incorporates recent work on maximum likelihood PCA (MLPCA) into conventional PCA-based monitoring. The proposed approach allows the simultaneous and consistent estimation of the PCA model plane, its dimension and the error covariance matrix. This paper also invokes recent work on monitoring non-Gaussian processes to extract unknown Gaussian as well as non-Gaussian source signals from recorded process data. By contrasting the unified framework with PCA-based process monitoring using a simulation example and recorded data from two industrial processes, the proposed approach produced more accurate and/or sensitive monitoring models.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 104, Issue 2, 15 December 2010, Pages 223–232
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 104, Issue 2, 15 December 2010, Pages 223–232
نویسندگان
Thiago Feital, Uwe Kruger, Lei Xie, Udo Schubert, Enrique Luis Lima, José Carlos Pinto,