| 1 |
Improved nonlinear process monitoring based on ensemble KPCA with local structure analysis
Cui P, Zhan CJ, Yang YP
Chemical Engineering Research & Design, 142, 355, 2019 |
| 2 |
Gaussian feature learning based on variational autoencoder for improving nonlinear process monitoring
Zhang ZH, Jiang T, Zhan CJ, Yang YP
Journal of Process Control, 75, 136, 2019 |
| 3 |
NONLINEAR PROCESS MONITORING USING KERNEL NONNEGATIVE MATRIX FACTORIZATION
Zhai LR, Zhang YW, Guan SP, Fu YJ, Feng L
Canadian Journal of Chemical Engineering, 96(2), 554, 2018 |
| 4 |
Fault detection based on augmented kernel Mahalanobis distance for nonlinear dynamic processes
Shang J, Chen MY, Zhang HW
Computers & Chemical Engineering, 109, 311, 2018 |
| 5 |
Automated feature learning for nonlinear process monitoring - An approach using stacked denoising autoencoder and k-nearest neighbor rule
Zhang ZH, Jiang T, Li SH, Yang YP
Journal of Process Control, 64, 49, 2018 |
| 6 |
A Sparse PCA for Nonlinear Fault Diagnosis and Robust Feature Discovery of Industrial Processes
Yu HY, Khan F, Garaniya V
AIChE Journal, 62(5), 1494, 2016 |
| 7 |
Process monitoring through manifold regularization-based GMM with global/local information
Yu JB
Journal of Process Control, 45, 84, 2016 |
| 8 |
Improved fault detection in nonlinear chemical processes using WKPCA-SVDD
Jiang Q, Yan X
Korean Journal of Chemical Engineering, 31(11), 1935, 2014 |
| 9 |
Learning a data-dependent kernel function for KPCA-based nonlinear process monitoring
Shao JD, Rong G, Lee JM
Chemical Engineering Research & Design, 87(11A), 1471, 2009 |
| 10 |
Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM
Zhang YW
Chemical Engineering Science, 64(5), 801, 2009 |
