Gang Li
Gang Li
Postdoctor in Department of Chemical Engineering in USC
Verified email at usc.edu - Homepage
Title
Cited by
Cited by
Year
Total projection to latent structures for process monitoring
D Zhou, G Li, SJ Qin
AIChE Journal 56 (1), 168-178, 2010
3452010
Geometric properties of partial least squares for process monitoring
G Li, SJ Qin, D Zhou
Automatica 46 (1), 204-210, 2010
2782010
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
1312010
Reconstruction based fault prognosis for continuous processes
G Li, SJ Qin, Y Ji, D Zhou
Control Engineering Practice 18 (10), 1211-1219, 2010
1092010
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
1082014
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
882011
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
812013
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
702009
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
632015
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
612013
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
572010
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
522016
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
442016
Nonstationarity and cointegration tests for fault detection of dynamic processes
G Li, SJ Qin, T Yuan
IFAC Proceedings Volumes 47 (3), 10616-10621, 2014
242014
Comparative study on monitoring schemes for non-Gaussian distributed processes
G Li, SJ Qin
Journal of Process Control 67, 69-82, 2018
232018
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
212014
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
202014
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
182014
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
122015
Reconstruction based fault prognosis for continuous processe
G Li, SJ Qin, Y Ji, D Zhou
IFAC Proceedings Volumes 42 (8), 1019-1024, 2009
92009
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