| Nonlinear process fault diagnosis based on serial principal component analysis X Deng, X Tian, S Chen, CJ Harris IEEE transactions on neural networks and learning systems 29 (3), 560-572, 2018 | 188 | 2018 |
| Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis X Deng, X Tian, S Chen Chemometrics and Intelligent Laboratory Systems 127, 195-209, 2013 | 141 | 2013 |
| Multiway kernel independent component analysis based on feature samples for batch process monitoring X Tian, X Zhang, X Deng, S Chen Neurocomputing 72 (7-9), 1584-1596, 2009 | 123 | 2009 |
| Deep Principal Component Analysis Based on Layerwise Feature Extraction and Its Application to Nonlinear Process Monitoring X Deng, X Tian, S Chen, CJ Harris IEEE Transactions on Control Systems Technology 27 (6), 2526-2540, 2019 | 106 | 2019 |
| Nonlinear process fault pattern recognition using statistics kernel PCA similarity factor X Deng, X Tian Neurocomputing 121, 298-308, 2013 | 84 | 2013 |
| Anomaly detection using improved deep SVDD model with data structure preservation Z Zhang, X Deng Pattern Recognition Letters 148, 1-6, 2021 | 83 | 2021 |
| Online soft sensor design using local partial least squares models with adaptive process state partition W Shao, X Tian, P Wang, X Deng, S Chen Chemometrics and Intelligent Laboratory Systems 144, 108-121, 2015 | 79 | 2015 |
| Fault discriminant enhanced kernel principal component analysis incorporating prior fault information for monitoring nonlinear processes X Deng, X Tian, S Chen, CJ Harris Chemometrics and Intelligent Laboratory Systems 162, 21-34, 2017 | 72 | 2017 |
| Fault detection of multimode non-Gaussian dynamic process using dynamic Bayesian independent component analysis Y Xu, X Deng Neurocomputing 200, 70-79, 2016 | 64 | 2016 |
| Incipient fault detection for nonlinear processes based on dynamic multi-block probability related kernel principal component analysis P Cai, X Deng ISA transactions 105, 210-220, 2020 | 62 | 2020 |
| State-of-Health Prediction For Lithium-Ion Batteries With Multiple Gaussian Process Regression Model X Zheng, X Deng IEEE Access 7, 150383-150394, 2019 | 61 | 2019 |
| Modified kernel principal component analysis using double-weighted local outlier factor and its application to nonlinear process monitoring X Deng, L Wang ISA transactions 72, 218-228, 2018 | 59 | 2018 |
| Sparse kernel locality preserving projection and its application in nonlinear process fault detection D Xiaogang, T Xuemin Chinese Journal of Chemical Engineering 21 (2), 163-170, 2013 | 59 | 2013 |
| Batch Process Monitoring Based on Multiway Global Preserving Kernel Slow Feature Analysis H Zhang, X Tian, X Deng IEEE Access 5, 2696-2710, 2017 | 55 | 2017 |
| Process fault detection based on dynamic kernel slow feature analysis N Zhang, X Tian, L Cai, X Deng Computers & Electrical Engineering 41, 9-17, 2015 | 49 | 2015 |
| Two-step localized kernel principal component analysis based incipient fault diagnosis for nonlinear industrial processes X Deng, P Cai, Y Cao, P Wang Industrial & Engineering Chemistry Research 59 (13), 5956-5968, 2020 | 40 | 2020 |
| Multiphase batch process with transitions monitoring based on global preserving statistics slow feature analysis H Zhang, X Tian, X Deng, Y Cao Neurocomputing 293, 64-86, 2018 | 40 | 2018 |
| Incipient fault detection for chemical processes using two-dimensional weighted SLKPCA X Deng, J Deng Industrial & Engineering Chemistry Research 58 (6), 2280-2295, 2019 | 38 | 2019 |
| Double-Level Locally Weighted Extreme Learning Machine for Soft Sensor Modeling of Complex Nonlinear Industrial Processes X Zhang, X Deng, P Wang IEEE Sensors Journal 21 (2), 1897-1905, 2021 | 37 | 2021 |
| Multimode process fault detection using local neighborhood similarity analysis X Deng, X Tian Chinese Journal of Chemical Engineering 22 (11-12), 1260-1267, 2014 | 37 | 2014 |
