کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
688715 1460365 2016 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Nonlinear process monitoring based on kernel global–local preserving projections
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی تکنولوژی و شیمی فرآیندی
پیش نمایش صفحه اول مقاله
Nonlinear process monitoring based on kernel global–local preserving projections
چکیده انگلیسی


• A new nonlinear dimensionality reduction method called KGLPP is proposed.
• KPCA and KLPP are unified in the KGLPP framework.
• A KGLPP-based monitoring method is proposed for nonlinear processes.
• The performance of KGLPP is much better than KPCA and KLPP.

A new nonlinear dimensionality reduction method called kernel global–local preserving projections (KGLPP) is developed and applied for fault detection. KGLPP has the advantage of preserving global and local data structures simultaneously. The kernel principal component analysis (KPCA), which only preserves the global Euclidean structure of data, and the kernel locality preserving projections (KLPP), which only preserves the local neighborhood structure of data, are unified in the KGLPP framework. KPCA and KLPP can be easily derived from KGLPP by choosing some particular values of parameters. As a result, KGLPP is more powerful than KPCA and KLPP in capturing useful data characteristics. A KGLPP-based monitoring method is proposed for nonlinear processes. T2 and SPE statistics are constructed in the feature space for fault detection. Case studies in a nonlinear system and in the Tennessee Eastman process demonstrate that the KGLPP-based method significantly outperforms KPCA, KLPP and GLPP-based methods, in terms of higher fault detection rates and better fault sensitivity.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Process Control - Volume 38, February 2016, Pages 11–21
نویسندگان
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