کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
167055 1423397 2015 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes
ترجمه فارسی عنوان
تجزیه و تحلیل اجزای اصلی احتمالاتی برای شناسایی خطاهای فرایندهای شیمیایی چندبعدی
کلمات کلیدی
مانیتورینگ فرایند چند منظوره، تجزیه و تحلیل اجزای اصلی احتمالاتی مخلوط، همبستگی مدل، تشخیص گسل
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی مهندسی شیمی (عمومی)
چکیده انگلیسی

A novel approach named aligned mixture probabilistic principal component analysis (AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations, the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis (MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chinese Journal of Chemical Engineering - Volume 23, Issue 8, August 2015, Pages 1357–1363
نویسندگان
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