Nonlinear process monitoring based on kernel global–local preserving projections. (February 2016)

• Record Type: Journal Article
• Authors: Luo, Lijia; Bao, Shiyi; Mao, Jianfeng; Tang, Di
• Is Part Of: Journal of process control. Volume 38(2016:Feb.)
• Page Start: 11
• Page End: 21
• Publication Date: 2016-02
• Subjects: Process monitoring -- Dimensionality reduction -- Kernel -- Fault detection -- KGLPP; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2015.12.005
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: 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. Abstract: 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. T 2 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.