| 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 |
Decentralized dynamic process monitoring based on manifold regularized slow feature analysis
Xu X, Ding JL
Journal of Process Control, 98, 79, 2021 |
| 4 |
Anomaly detection in a hyper-compressor in low-density polyethylene manufacturing processes using WPCA-based principal component control limit
Park BE, Kim JS, Lee JK, Lee IB
Korean Journal of Chemical Engineering, 37(1), 11, 2020 |
| 5 |
Semi-supervised dynamic latent variable modeling: I/O probabilistic slow feature analysis approach
Fan L, Kodamana H, Huang BA
AIChE Journal, 65(3), 964, 2019 |
| 6 |
Extracting Dissimilarity of Slow Feature Analysis between Normal and Different Faults for Monitoring Process Status and Fault Diagnosis
Zheng HY, Yan XF
Journal of Chemical Engineering of Japan, 52(3), 283, 2019 |
| 7 |
Comprehensive process decomposition for closed-loop process monitoring with quality-relevant slow feature analysis
Qin Y, Zhao CH
Journal of Process Control, 77, 141, 2019 |
| 8 |
A Full-Condition Monitoring Method for Nonstationary Dynamic Chemical Processes with Cointegration and Slow Feature Analysis
Zhao CH, Huang B
AIChE Journal, 64(5), 1662, 2018 |
| 9 |
Extracting dynamic features with switching models for process data analytics and application in soft sensing
Ma YJ, Huang B
AIChE Journal, 64(6), 2037, 2018 |
| 10 |
Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis
Shang C, Yang F, Gao XQ, Huang XL, Suykens JAK, Huang DX
AIChE Journal, 61(11), 3666, 2015 |
