L
Lijia Luo
Researcher at Zhejiang University of Technology
Publications - 55
Citations - 795
Lijia Luo is an academic researcher from Zhejiang University of Technology. The author has contributed to research in topics: Fault detection and isolation & Reactor pressure vessel. The author has an hindex of 15, co-authored 53 publications receiving 573 citations. Previous affiliations of Lijia Luo include Chinese Ministry of Education & University of Delaware.
Papers
More filters
Journal ArticleDOI
Process Monitoring with Global–Local Preserving Projections
TL;DR: In this paper, a novel dimensionality reduction algorithm named "global-local-preserving projections" (GLPP) is proposed, which aims at preserving both global and local structures of the data set by solving a dual-objective optimization function.
Journal ArticleDOI
Nonlinear process monitoring based on kernel global–local preserving projections
TL;DR: In this paper, a new nonlinear dimensionality reduction method called kernel global-local-preserving projections (KGLPP) is developed and applied for fault detection, which has the advantage of preserving global and local data structures simultaneously.
Journal ArticleDOI
Quality prediction and quality-relevant monitoring with multilinear PLS for batch processes
TL;DR: The results indicate that the multilinear PLS method has higher predictive accuracy, better anti-noise capability and monitoring performance than the unfold-PLS method.
Journal ArticleDOI
Fault Detection and Diagnosis Based on Sparse PCA and Two-Level Contribution Plots
TL;DR: A novel process monitoring method is proposed based on sparse principal component analysis (SpPCA), where the selected sparse loading vectors classify all process variables into nonoverlapping groups according to variable correlations.
Journal ArticleDOI
Sparse Robust Principal Component Analysis with Applications to Fault Detection and Diagnosis
Lijia Luo,Shiyi Bao,Chudong Tong +2 more
TL;DR: A new PCA method is proposed that has the properties of robustness and sparsity at the same time, called sparse robust PCA (SRPCA), which is not only robust against outliers, but also can yield interpretable PCs.