| 1 |
Two-step support vector data description for dynamic, non-linear, and non-Gaussian processes monitoring
Zhang YF, Li XS
Canadian Journal of Chemical Engineering, 98(10), 2109, 2020 |
| 2 |
An incipient fault detection and self-learning identification method based on robust SVDD and RBM-PNN
Zhang CF, Peng KX, Dong J
Journal of Process Control, 85, 173, 2020 |
| 3 |
Acoustical damage detection of wind turbine blade using the improved incremental support vector data description
Chen B, Yu SH, Yu Y, Zhou YL
Renewable Energy, 156, 548, 2020 |
| 4 |
Incipient Fault Detection Based on Exergy Efficiency and Support Vector Data Description
Zhou MF, Liu ZH, Cai YJ, Pan HT
Journal of Chemical Engineering of Japan, 52(6), 562, 2019 |
| 5 |
Batch process monitoring based on WGNPE-GSVDD related and independent variables
Hui YY, Zhao XQ
Chinese Journal of Chemical Engineering, 26(12), 2549, 2018 |
| 6 |
Optimal false alarm controlled support vector data description for multivariate process monitoring
Kim Y, Kim SB
Journal of Process Control, 65, 1, 2018 |
| 7 |
Comparative study on monitoring schemes for non-Gaussian distributed processes
Li G, Qin SJ
Journal of Process Control, 67, 69, 2018 |
| 8 |
An improved fault detection method for incipient centrifugal chiller faults using the PCA-R-SVDD algorithm
Li GN, Hu YP, Chen HX, Shen LM, Li HR, Hu M, Liu JY, Sun KZ
Energy and Buildings, 116, 104, 2016 |
| 9 |
A sensor fault detection and diagnosis strategy for screw chiller system using support vector data description-based D-statistic and DV-contribution plots
Li GN, Hu YP, Chen HX, Li HR, Hu M, Guo YB, Shi SB, Hu WJ
Energy and Buildings, 133, 230, 2016 |
| 10 |
Related and independent variable fault detection based on KPCA and SVDD
Huang J, Yan XF
Journal of Process Control, 39, 88, 2016 |
