| 1 |
Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder
Wang YL, Yang HB, Yuan XF, Shardt YAW, Yang CH, Gui WH
Journal of Process Control, 92, 79, 2020 |
| 2 |
Gaussian Discriminative Analysis aided GAN for imbalanced big data augmentation and fault classification
Zhuo Y, Ge ZQ
Journal of Process Control, 92, 271, 2020 |
| 3 |
Incorporate active learning to semi-supervised industrial fault classification
Yin LL, Wang HG, Fan WH, Kou L, Lin TY, Xiao YY
Journal of Process Control, 78, 88, 2019 |
| 4 |
K-means Bayes algorithm for imbalanced fault classification and big data application
Chen GC, Liu Y, Ge ZQ
Journal of Process Control, 81, 54, 2019 |
| 5 |
PV shading fault detection and classification based on I-V curve using principal component analysis: Application to isolated PV system
Fadhel S, Delpha C, Diallo D, Bahri I, Migan A, Trabelsi M, Mimouni MF
Solar Energy, 179, 1, 2019 |
| 6 |
Recursive Spectral Meta-Learner for Online Combining Different Fault Classifiers
Chen MY, Shang J
IEEE Transactions on Automatic Control, 63(2), 586, 2018 |
| 7 |
Weighted random forests for fault classification in industrial processes with hierarchical clustering model selection
Liu Y, Ge ZQ
Journal of Process Control, 64, 62, 2018 |
| 8 |
Improving classification-based diagnosis of batch processes through data selection and appropriate pretreatment
Gins G, Van den Kerkhof P, Vanlaer J, Van Impe JFM
Journal of Process Control, 26, 90, 2015 |
| 9 |
A Support Vector Clustering-Based Probabilistic Method for Unsupervised Fault Detection and Classification of Complex Chemical Processes Using Unlabeled Data
Yu J
AIChE Journal, 59(2), 407, 2013 |
| 10 |
Fault diagnosis by Locality Preserving Discriminant Analysis and its kernel variation
Rong G, Liu SY, Shao JD
Computers & Chemical Engineering, 49, 105, 2013 |
