| Total projection to latent structures for process monitoring D Zhou, G Li, SJ Qin AIChE journal 56 (1), 168-178, 2010 | 503 | 2010 |
| Geometric properties of partial least squares for process monitoring G Li, SJ Qin, D Zhou Automatica 46 (1), 204-210, 2010 | 390 | 2010 |
| A new method of dynamic latent-variable modeling for process monitoring G Li, SJ Qin, D Zhou IEEE Transactions on Industrial Electronics 61 (11), 6438-6445, 2014 | 194 | 2014 |
| Generalized reconstruction-based contributions for output-relevant fault diagnosis with application to the Tennessee Eastman process G Li, CF Alcala, SJ Qin, D Zhou IEEE transactions on control systems technology 19 (5), 1114-1127, 2010 | 157 | 2010 |
| Reconstruction based fault prognosis for continuous processes G Li, SJ Qin, Y Ji, D Zhou Control Engineering Practice 18 (10), 1211-1219, 2010 | 143 | 2010 |
| Quality relevant data-driven modeling and monitoring of multivariate dynamic processes: The dynamic T-PLS approach G Li, B Liu, SJ Qin, D Zhou IEEE transactions on neural networks 22 (12), 2262-2271, 2011 | 125 | 2011 |
| Quality-related process monitoring based on total kernel PLS model and its industrial application K Peng, K Zhang, G Li Mathematical Problems in Engineering 2013, 2013 | 123 | 2013 |
| Data-driven root cause diagnosis of faults in process industries G Li, SJ Qin, T Yuan Chemometrics and Intelligent Laboratory Systems 159, 1-11, 2016 | 118 | 2016 |
| Contribution rate plot for nonlinear quality-related fault diagnosis with application to the hot strip mill process K Peng, K Zhang, G Li, D Zhou Control Engineering Practice 21 (4), 360-369, 2013 | 106 | 2013 |
| Autoregressive dynamic latent variable models for process monitoring L Zhou, G Li, Z Song, SJ Qin IEEE Transactions on Control Systems Technology 25 (1), 366-373, 2016 | 99 | 2016 |
| Adaptive total PLS based quality-relevant process monitoring with application to the Tennessee Eastman process J Dong, K Zhang, Y Huang, G Li, K Peng Neurocomputing 154, 77-85, 2015 | 95 | 2015 |
| Total PLS based contribution plots for fault diagnosis L Gang, QIN Si-Zhao, JI Yin-Dong, Z Dong-Hua Acta Automatica Sinica 35 (6), 759-765, 2009 | 93 | 2009 |
| Output relevant fault reconstruction and fault subspace extraction in total projection to latent structures models G Li, S Joe Qin, D Zhou Industrial & Engineering Chemistry Research 49 (19), 9175-9183, 2010 | 70 | 2010 |
| Nonstationarity and cointegration tests for fault detection of dynamic processes G Li, SJ Qin, T Yuan IFAC Proceedings Volumes 47 (3), 10616-10621, 2014 | 42 | 2014 |
| Comparative study on monitoring schemes for non-Gaussian distributed processes G Li, SJ Qin Journal of Process Control 67, 69-82, 2018 | 38 | 2018 |
| Multi-directional reconstruction based contributions for root-cause diagnosis of dynamic processes G Li, SJ Qin, T Chai 2014 American Control Conference, 3500-3505, 2014 | 29 | 2014 |
| Dynamic time warping based causality analysis for root-cause diagnosis of nonstationary fault processes G Li, T Yuan, SJ Qin, T Chai IFAC-PapersOnLine 48 (8), 1288-1293, 2015 | 26 | 2015 |
| New kernel independent and principal components analysis‐based process monitoring approach with application to hot strip mill process K Peng, K Zhang, X He, G Li, X Yang IET Control Theory & Applications 8 (16), 1723-1731, 2014 | 24 | 2014 |
| Online contribution rate based fault diagnosis for nonlinear industrial processes P Kai-Xiang, K Zhang, LI Gang Acta Automatica Sinica 40 (3), 423-430, 2014 | 20 | 2014 |
| Kernel dynamic latent variable model for process monitoring with application to hot strip mill process G Li, K Peng, T Yuan, M Zhong Chemometrics and Intelligent Laboratory Systems 171, 218-225, 2017 | 12 | 2017 |
S. Joe QinLingnan University, Hong Kong, President, Member of EASA, Fellow of HKAES, NAI, IEEE, IFAC, AIChEVerified email at ln.edu.hk
