A survey of industrial model predictive control technology SJ Qin, TA Badgwell Control engineering practice 11 (7), 733-764, 2003 | 6486 | 2003 |
Statistical process monitoring: basics and beyond S Joe Qin Journal of Chemometrics: A Journal of the Chemometrics Society 17 (8‐9), 480-502, 2003 | 1813 | 2003 |
An overview of industrial model predictive control technology SJ Qin, TA Badgwell AIche symposium series 93 (316), 232-256, 1997 | 1536 | 1997 |
Survey on data-driven industrial process monitoring and diagnosis SJ Qin Annual reviews in control 36 (2), 220-234, 2012 | 1481 | 2012 |
Recursive PCA for adaptive process monitoring W Li, HH Yue, S Valle-Cervantes, SJ Qin Journal of process control 10 (5), 471-486, 2000 | 1047 | 2000 |
Nonlinear Model Predictive Control, chap. An overview of nonlinear model predictive control applications S Qin, T Badgwell Birkhäuser Verlag, Boston, MA, 2000 | 829* | 2000 |
An overview of nonlinear model predictive control applications SJ Qin, TA Badgwell Nonlinear model predictive control, 369-392, 2000 | 813 | 2000 |
Identification of faulty sensors using principal component analysis R Dunia, SJ Qin, TF Edgar, TJ McAvoy AIChE Journal 42 (10), 2797-2812, 1996 | 781 | 1996 |
An overview of subspace identification SJ Qin Computers & chemical engineering 30 (10-12), 1502-1513, 2006 | 779 | 2006 |
Recursive PLS algorithms for adaptive data modeling SJ Qin Computers & Chemical Engineering 22 (4-5), 503-514, 1998 | 778 | 1998 |
Nonlinear predictive control and moving horizon estimation—an introductory overview F Allgöwer, TA Badgwell, JS Qin, JB Rawlings, SJ Wright Advances in control, 391-449, 1999 | 731 | 1999 |
Selection of the number of principal components: the variance of the reconstruction error criterion with a comparison to other methods S Valle, W Li, SJ Qin Industrial & Engineering Chemistry Research 38 (11), 4389-4401, 1999 | 689 | 1999 |
Nonlinear PLS modeling using neural networks SJ Qin, TJ McAvoy Computers & Chemical Engineering 16 (4), 379-391, 1992 | 666 | 1992 |
Reconstruction-based fault identification using a combined index HH Yue, SJ Qin Industrial & engineering chemistry research 40 (20), 4403-4414, 2001 | 608 | 2001 |
Reconstruction-based contribution for process monitoring CF Alcala, SJ Qin Automatica 45 (7), 1593-1600, 2009 | 593 | 2009 |
Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models J Yu, SJ Qin AIChE Journal 54 (7), 1811-1829, 2008 | 557 | 2008 |
Subspace approach to multidimensional fault identification and reconstruction R Dunia, SJ Qin AIChE Journal 44 (8), 1813-1831, 1998 | 556 | 1998 |
Total projection to latent structures for process monitoring D Zhou, G Li, SJ Qin AIChE Journal 56 (1), 168-178, 2010 | 511 | 2010 |
Control performance monitoring—a review and assessment SJ Qin Computers & Chemical Engineering 23 (2), 173-186, 1998 | 506 | 1998 |
Fault detection and diagnosis based on modified independent component analysis JM Lee, SJ Qin, IB Lee AIChE journal 52 (10), 3501-3514, 2006 | 489 | 2006 |