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 |
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 |
3 |
MRS-kNN fault detection method for multirate sampling process based variable grouping threshold Feng J, Li KQ Journal of Process Control, 85, 149, 2020 |
4 |
Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes Yu JB, Zhang CY Journal of Process Control, 92, 119, 2020 |
5 |
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 |
6 |
DISCRIMINANT DIFFUSION MAPS BASED K-NEAREST-NEIGHBOUR FOR BATCH PROCESS FAULT DETECTION Li Y, Liu YD, Zhang C Canadian Journal of Chemical Engineering, 96(2), 484, 2018 |
7 |
Enhanced fault detection for nonlinear processes using modified kernel partial least squares and the statistical local approach Wang L Canadian Journal of Chemical Engineering, 96(5), 1116, 2018 |
8 |
Dynamic mutual information similarity based transient process identification and fault detection He YC, Zhou L, Ge ZQ, Song ZH Canadian Journal of Chemical Engineering, 96(7), 1541, 2018 |
9 |
Online reduced kernel principal component analysis for process monitoring Fezai R, Mansouri M, Taouali O, Harkat MF, Bouguila N Journal of Process Control, 61, 1, 2018 |
10 |
Batch Process Monitoring Based on Fuzzy Segmentation of Multivariate Time-Series Tanatavikorn H, Yamashita Y Journal of Chemical Engineering of Japan, 50(1), 53, 2017 |