Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and Bayesian inference Q Jiang, X Yan, B Huang IEEE Transactions on Industrial Electronics 63 (1), 377-386, 2015 | 341 | 2015 |
Review and perspectives of data-driven distributed monitoring for industrial plant-wide processes Q Jiang, X Yan, B Huang Industrial & Engineering Chemistry Research 58 (29), 12899-12912, 2019 | 257 | 2019 |
Parallel PCA–KPCA for nonlinear process monitoring Q Jiang, X Yan Control Engineering Practice 80, 17-25, 2018 | 193 | 2018 |
Plant-wide process monitoring based on mutual information–multiblock principal component analysis Q Jiang, X Yan ISA transactions 53 (5), 1516-1527, 2014 | 129 | 2014 |
Fault detection and diagnosis in chemical processes using sensitive principal component analysis Q Jiang, X Yan, W Zhao Industrial & Engineering Chemistry Research 52 (4), 1635-1644, 2013 | 119 | 2013 |
Data-driven distributed local fault detection for large-scale processes based on the GA-regularized canonical correlation analysis Q Jiang, SX Ding, Y Wang, X Yan IEEE Transactions on Industrial Electronics 64 (10), 8148-8157, 2017 | 111 | 2017 |
Distributed monitoring for large-scale processes based on multivariate statistical analysis and Bayesian method Q Jiang, B Huang Journal of Process Control 46, 75-83, 2016 | 111 | 2016 |
Data-driven batch-end quality modeling and monitoring based on optimized sparse partial least squares Q Jiang, X Yan, H Yi, F Gao IEEE Transactions on Industrial Electronics 67 (5), 4098-4107, 2019 | 105 | 2019 |
Nonlinear plant-wide process monitoring using MI-spectral clustering and Bayesian inference-based multiblock KPCA Q Jiang, X Yan Journal of Process Control 32, 38-50, 2015 | 105 | 2015 |
Just‐in‐time reorganized PCA integrated with SVDD for chemical process monitoring Q Jiang, X Yan AIChE Journal 60 (3), 949-965, 2014 | 105 | 2014 |
Monitoring multi-mode plant-wide processes by using mutual information-based multi-block PCA, joint probability, and Bayesian inference Q Jiang, X Yan Chemometrics and intelligent laboratory systems 136, 121-137, 2014 | 101 | 2014 |
GMM and optimal principal components-based Bayesian method for multimode fault diagnosis Q Jiang, B Huang, X Yan Computers & Chemical Engineering 84, 338-349, 2016 | 94 | 2016 |
Neural network aided approximation and parameter inference of non-Markovian models of gene expression Q Jiang, X Fu, S Yan, R Li, W Du, Z Cao, F Qian, R Grima Nature communications 12 (1), 2618, 2021 | 93 | 2021 |
Local–global modeling and distributed computing framework for nonlinear plant-wide process monitoring with industrial big data Q Jiang, S Yan, H Cheng, X Yan IEEE transactions on neural networks and learning systems 32 (8), 3355-3365, 2020 | 91 | 2020 |
Multimode process monitoring using variational Bayesian inference and canonical correlation analysis Q Jiang, X Yan IEEE Transactions on Automation Science and Engineering 16 (4), 1814-1824, 2019 | 78 | 2019 |
Learning deep correlated representations for nonlinear process monitoring Q Jiang, X Yan IEEE Transactions on Industrial Informatics 15 (12), 6200-6209, 2018 | 65 | 2018 |
Multivariate statistical monitoring of key operation units of batch processes based on time-slice CCA Q Jiang, F Gao, H Yi, X Yan IEEE Transactions on Control Systems Technology 27 (3), 1368-1375, 2018 | 58 | 2018 |
Data-Driven Two-Dimensional Deep Correlated Representation Learning for Nonlinear Batch Process Monitoring Q Jiang, S Yan, X Yan, H Yi, F Gao IEEE Transactions on Industrial Informatics 16 (4), 2839-2848, 2019 | 52 | 2019 |
Multiblock independent component analysis integrated with Hellinger distance and Bayesian inference for non-Gaussian plant-wide process monitoring Q Jiang, B Wang, X Yan Industrial & Engineering Chemistry Research 54 (9), 2497-2508, 2015 | 51 | 2015 |
Bayesian fault diagnosis with asynchronous measurements and its application in networked distributed monitoring Q Jiang, B Huang, SX Ding, X Yan IEEE Transactions on Industrial Electronics 63 (10), 6316-6324, 2016 | 50 | 2016 |