کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5132289 1491510 2017 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Slow feature analysis based on online feature reordering and feature selection for dynamic chemical process monitoring
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
Slow feature analysis based on online feature reordering and feature selection for dynamic chemical process monitoring
چکیده انگلیسی


- SFA is employed to extract the slowly varying features embedded in the dynamic process data.
- An online feature reordering and feature selection strategy is proposed to take full advantages of online fault information.
- The proposed algorithm has better monitoring performance than traditional methods.

This study considers the insufficiency of traditional monitoring methods to eliminate dynamics, and proposes a novel online feature reordering- and feature selection-based slow feature analysis (SFA) algorithm. The SFA algorithm explores the process dynamics from the view of inner variation of data to extract the slowly varying features. The extracted SFs are considered as the representations of steady- and dynamic-state processes. Online feature reordering and feature selection strategies maximize online fault information and can be used to perform fault detection operation. The proposed method is applied to two simulated processes. Monitoring results show that the proposed method has better monitoring results than those of traditional methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 169, 15 October 2017, Pages 1-11
نویسندگان
, , ,