| Deep learning with spatiotemporal attention-based LSTM for industrial soft sensor model development X Yuan, L Li, YAW Shardt, Y Wang, C Yang IEEE Transactions on Industrial Electronics 68 (5), 4404-4414, 2020 | 386 | 2020 |
| A just-in-time-learning-aided canonical correlation analysis method for multimode process monitoring and fault detection Z Chen, C Liu, SX Ding, T Peng, C Yang, W Gui, YAW Shardt IEEE Transactions on Industrial Electronics 68 (6), 5259-5270, 2020 | 123 | 2020 |
| Improved canonical correlation analysis-based fault detection methods for industrial processes Z Chen, K Zhang, SX Ding, YAW Shardt, Z Hu Journal of Process Control 41, 26-34, 2016 | 119 | 2016 |
| Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder Y Wang, H Yang, X Yuan, YAW Shardt, C Yang, W Gui Journal of Process Control 92, 79-89, 2020 | 114 | 2020 |
| Soft sensor model for dynamic processes based on multichannel convolutional neural network X Yuan, S Qi, YAW Shardt, Y Wang, C Yang, W Gui Chemometrics and Intelligent Laboratory Systems 203, 104050, 2020 | 102 | 2020 |
| Determining the state of a process control system: Current trends and future challenges Y Shardt, Y Zhao, F Qi, K Lee, X Yu, B Huang, S Shah The Canadian Journal of Chemical Engineering 90 (2), 217-245, 2012 | 90 | 2012 |
| Quality-driven regularization for deep learning networks and its application to industrial soft sensors C Ou, H Zhu, YAW Shardt, L Ye, X Yuan, Y Wang, C Yang IEEE Transactions on Neural Networks and Learning Systems, 2022 | 88 | 2022 |
| A new soft-sensor-based process monitoring scheme incorporating infrequent KPI measurements YAW Shardt, H Hao, SX Ding IEEE Transactions on Industrial Electronics 62 (6), 3843-3851, 2014 | 85 | 2014 |
| Statistics for Chemical and Process Engineers YAW Shardt Springer International Publishing, 2022 | 62 | 2022 |
| Graph neural network-based fault diagnosis: a review Z Chen, J Xu, C Alippi, SX Ding, Y Shardt, T Peng, C Yang arXiv preprint arXiv:2111.08185, 2021 | 62 | 2021 |
| An incipient fault detection approach via detrending and denoising Z He, YAW Shardt, D Wang, B Hou, H Zhou, J Wang Control Engineering Practice 74, 1-12, 2018 | 55 | 2018 |
| A KPI-based process monitoring and fault detection framework for large-scale processes K Zhang, YAW Shardt, Z Chen, X Yang, SX Ding, K Peng ISA transactions 68, 276-286, 2017 | 54 | 2017 |
| A KPI-based soft sensor development approach incorporating infrequent, variable time delayed measurements X Yang, Y Zhang, YAW Shardt, X Li, J Cui, C Tong IEEE Transactions on Control Systems Technology 28 (6), 2523-2531, 2019 | 45 | 2019 |
| Closed-loop identification with routine operating data: Effect of time delay and sampling time YAW Shardt, B Huang Journal of Process Control 21 (7), 997-1010, 2011 | 45 | 2011 |
| Closed-loop identification condition for ARMAX models using routine operating data YAW Shardt, B Huang Automatica 47 (7), 1534-1537, 2011 | 44 | 2011 |
| Data quality assessment of routine operating data for process identification YAW Shardt, B Huang Computers & chemical engineering 55, 19-27, 2013 | 42 | 2013 |
| Estimating the unknown time delay in chemical processes S Mehrkanoon, YAW Shardt, JAK Suykens, SX Ding Engineering Applications of Artificial Intelligence 55, 219-230, 2016 | 39 | 2016 |
| Dynamic system modelling and process monitoring based on long-term dependency slow feature analysis X Gao, YAW Shardt Journal of Process Control 105, 27-47, 2021 | 33 | 2021 |
| Modelling the strip thickness in hot steel rolling mills using least‐squares support vector machines YAW Shardt, S Mehrkanoon, K Zhang, X Yang, J Suykens, SX Ding, ... The Canadian Journal of Chemical Engineering 96 (1), 171-178, 2018 | 31 | 2018 |
| Assessment of T2-and Q-statistics for detecting additive and multiplicative faults in multivariate statistical process monitoring K Zhang, SX Ding, YAW Shardt, Z Chen, K Peng Journal of the Franklin Institute 354 (2), 668-688, 2017 | 29 | 2017 |
Ding, Steven X.AKS, University of Duisburg-EssenVerified email at uni-due.de
