| 1 |
One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes
Chen SM, Yu JB, Wang SJ
Journal of Process Control, 87, 54, 2020 |
| 2 |
Intelligent simultaneous fault diagnosis for solid oxide fuel cell system based on deep learning
Zhang ZH, Li SH, Xiao YW, Yang YP
Applied Energy, 233, 930, 2019 |
| 3 |
Gaussian feature learning based on variational autoencoder for improving nonlinear process monitoring
Zhang ZH, Jiang T, Zhan CJ, Yang YP
Journal of Process Control, 75, 136, 2019 |
| 4 |
A deep autoencoder feature learning method for process pattern recognition
Yu JB, Zheng XY, Wang SJ
Journal of Process Control, 79, 1, 2019 |
| 5 |
Feature learning and process monitoring of injection molding using convolution-deconvolution auto encoders
Mao T, Zhang Y, Ruan YF, Gao H, Zhou HM, Li DQ
Computers & Chemical Engineering, 118, 77, 2018 |
| 6 |
Automated feature learning for nonlinear process monitoring - An approach using stacked denoising autoencoder and k-nearest neighbor rule
Zhang ZH, Jiang T, Li SH, Yang YP
Journal of Process Control, 64, 49, 2018 |
| 7 |
Occupancy estimation with environmental sensing via non-iterative LRF feature learning in time and frequency domains
Zhu QC, Chen ZH, Masood MK, Soh YC
Energy and Buildings, 141, 125, 2017 |
