| 1 |
Research on TE process fault diagnosis method based on DBN and dropout
Wei YQ, Weng ZX
Canadian Journal of Chemical Engineering, 98(6), 1293, 2020 |
| 2 |
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 |
| 3 |
Model-free direct fault detection and classification
Hamadouche A
Journal of Process Control, 87, 130, 2020 |
| 4 |
Generalized moving variance filters for industrial alarm systems
Roohi MH, Chen TW
Journal of Process Control, 95, 75, 2020 |
| 5 |
Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder
Wang YL, Yang HB, Yuan XF, Shardt YAW, Yang CH, Gui WH
Journal of Process Control, 92, 79, 2020 |
| 6 |
Fault detection of uncertain nonlinear process using interval-valued data-driven approach
Harkat MF, Mansouri M, Nounou M, Nounou H
Chemical Engineering Science, 205, 36, 2019 |
| 7 |
Process fault diagnosis via the integrated use of graphical lasso and Markov random fields learning & inference
Kim C, Lee H, Lee WB
Computers & Chemical Engineering, 125, 460, 2019 |
| 8 |
A combined monitoring scheme with fuzzy logic filter for plant-wide Tennessee Eastman Process fault detection
Ammiche M, Kouadri A, Bakdi A
Chemical Engineering Science, 187, 269, 2018 |
| 9 |
Deep convolutional neural network model based chemical process fault diagnosis
Wu H, Zhao JS
Computers & Chemical Engineering, 115, 185, 2018 |
| 10 |
Locality preserving discriminative canonical variate analysis for fault diagnosis
Lu QG, Jiang BB, Gopaluni RB, Loewen PD, Braatz RD
Computers & Chemical Engineering, 117, 309, 2018 |
