| 1 |
Dynamic plant-wide process monitoring based on distributed slow feature analysis with inter-unit dissimilarity
Huang R, Li Z, Cao B
Korean Journal of Chemical Engineering, 39(2), 275, 2022 |
| 2 |
Dynamic nonlinear batch process fault detection and identification based on two-directional dynamic kernel slow feature analysis
Zhang HY, Deng XG, Zhang YC, Hou CJ, Li CD
Canadian Journal of Chemical Engineering, 99(1), 306, 2021 |
| 3 |
Deep neural network based recursive feature learning for nonlinear dynamic process monitoring
Zhu JZ, Shi HB, Song B, Tan S, Tao Y
Canadian Journal of Chemical Engineering, 98(4), 919, 2020 |
| 4 |
Enhanced high-order information extraction for multiphase batch process fault monitoring
Ding CH, Chang P, Kang O
Canadian Journal of Chemical Engineering, 98(10), 2187, 2020 |
| 5 |
Fault-tolerant control of flexible air-breathing hypersonic vehicles in linear ODE-beam systems
Zhao D, Jiang B, Yang H, Tao G
International Journal of Control, 93(4), 820, 2020 |
| 6 |
One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes
Chen SM, Yu JB, Wang SJ
Journal of Process Control, 87, 54, 2020 |
| 7 |
MRS-kNN fault detection method for multirate sampling process based variable grouping threshold
Feng J, Li KQ
Journal of Process Control, 85, 149, 2020 |
| 8 |
Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes
Yu JB, Zhang CY
Journal of Process Control, 92, 119, 2020 |
| 9 |
Dynamic process fault detection and diagnosis based on a combined approach of hidden Markov and Bayesian network model
Don MG, Khan F
Chemical Engineering Science, 201, 82, 2019 |
| 10 |
Fault diagnosis for distillation process based on CNN-DAE
Li CK, Zhao DF, Mu SJ, Zhang WH, Shi N, Li LN
Chinese Journal of Chemical Engineering, 27(3), 598, 2019 |
