| 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 |
Simultaneous fault detection and isolation using semi-supervised kernel nonnegative matrix factorization
Zhai LR, Jia QL
Canadian Journal of Chemical Engineering, 97(12), 3025, 2019 |
| 3 |
Feature space monitoring for smart manufacturing via statistics pattern analysis
He QP, Wang J, Shah D
Computers & Chemical Engineering, 126, 321, 2019 |
| 4 |
Distributed partial least squares based residual generation for statistical process monitoring
Tong CD, Lan T, Yu HZ, Peng X
Journal of Process Control, 75, 77, 2019 |
| 5 |
Generating optimal overlapping subsystems for distributed statistical fault detection subject to constraints
Khatib S, Daoutidis P
Journal of Process Control, 80, 143, 2019 |
| 6 |
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 |
| 7 |
Sequential local-based Gaussian mixture model for monitoring multiphase batch processes
Liu JX, Liu T, Chen JH
Chemical Engineering Science, 181, 101, 2018 |
| 8 |
Statistical process monitoring based on nonlocal and multiple neighborhoods preserving embedding model
Tong CD, Lan T, Shi XH, Chen YW
Journal of Process Control, 65, 34, 2018 |
| 9 |
Statistical process monitoring as a big data analytics tool for smart manufacturing
He QP, Wang J
Journal of Process Control, 67, 35, 2018 |
| 10 |
Real-time fault detection and diagnosis using sparse principal component analysis
Gajjar S, Kulahci M, Palazoglu A
Journal of Process Control, 67, 112, 2018 |
