| 1 |
Multi-block principal component analysis based on variable weight information and its application to multivariate process monitoring
Wang L, Deng XG
Canadian Journal of Chemical Engineering, 96(5), 1127, 2018 |
| 2 |
Sparse canonical variate analysis approach for process monitoring
Lu QG, Jiang BB, Gopaluni RB, Loewen PD, Braatz RD
Journal of Process Control, 71, 90, 2018 |
| 3 |
A sensor fault detection and diagnosis strategy for screw chiller system using support vector data description-based D-statistic and DV-contribution plots
Li GN, Hu YP, Chen HX, Li HR, Hu M, Guo YB, Shi SB, Hu WJ
Energy and Buildings, 133, 230, 2016 |
| 4 |
Slow feature analysis for monitoring and diagnosis of control performance
Shang C, Huang B, Yang F, Huang DX
Journal of Process Control, 39, 21, 2016 |
| 5 |
Improved fault detection and diagnosis using sparse global-local preserving projections
Bao SY, Luo LJ, Mao JF, Tang D
Journal of Process Control, 47, 121, 2016 |
| 6 |
A new process monitoring method based on noisy time structure independent component analysis
Cai LF, Tian XM
Chinese Journal of Chemical Engineering, 23(1), 162, 2015 |
| 7 |
Canonical variate analysis-based contributions for fault identification
Jiang BB, Huang DX, Zhu XX, Yang F, Braatz RD
Journal of Process Control, 26, 17, 2015 |
| 8 |
A new fault diagnosis method using fault directions in fisher discriminant analysis
He QP, Qin SJ, Wang J
AIChE Journal, 51(2), 555, 2005 |
