| 1 |
Modified non-Gaussian multivariate statistical process monitoring based on the Gaussian distribution transformation
Du WY, Zhang YW, Zhou W
Journal of Process Control, 85, 1, 2020 |
| 2 |
Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008-2017
Wang YQ, Si YB, Huang B, Lou ZJ
Canadian Journal of Chemical Engineering, 96(10), 2073, 2018 |
| 3 |
Sequential local-based Gaussian mixture model for monitoring multiphase batch processes
Liu JX, Liu T, Chen JH
Chemical Engineering Science, 181, 101, 2018 |
| 4 |
Real-time fault detection and diagnosis using sparse principal component analysis
Gajjar S, Kulahci M, Palazoglu A
Journal of Process Control, 67, 112, 2018 |
| 5 |
On the the use of reconstruction-based contribution for fault diagnosis
Ji HQ, He X, Zhou DH
Journal of Process Control, 40, 24, 2016 |
| 6 |
Multivariate Statistical Process Monitoring of Batch-to-Batch Startups
Yan ZB, Huang BL, Yao Y
AIChE Journal, 61(11), 3719, 2015 |
| 7 |
Multivariate fault isolation via variable selection in discriminant analysis
Kuang TH, Yan ZB, Yao Y
Journal of Process Control, 35, 30, 2015 |
| 8 |
Multivariate statistical monitoring as applied to clean-in-place (CIP) and steam-in-place (SIP) operations in biopharmaceutical manufacturing
Roy K, Undey C, Mistretta T, Naugle G, Sodhi M
Biotechnology Progress, 30(2), 505, 2014 |
| 9 |
Root cause analysis in multivariate statistical process monitoring: Integrating reconstruction-based multivariate contribution analysis with fuzzy-signed directed graphs
He B, Chen T, Yang XH
Computers & Chemical Engineering, 64, 167, 2014 |
| 10 |
Statistical root cause analysis of novel faults based on digraph models
Wan YM, Yang F, Lv N, Xu HP, Ye H, Li WC, Xu P, Song LM, Usadi AK
Chemical Engineering Research & Design, 91(1), 87, 2013 |
