Yuri A. W. Shardt
Yuri A. W. Shardt
Professor, TU Ilmenau
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
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
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
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
Statistics for Chemical and Process Engineers
YAW Shardt
Springer International Publishing, 2022
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
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
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
Closed-loop identification condition for ARMAX models using routine operating data
YAW Shardt, B Huang
Automatica 47 (7), 1534-1537, 2011
Data quality assessment of routine operating data for process identification
YAW Shardt, B Huang
Computers & chemical engineering 55, 19-27, 2013
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
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
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
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
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
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
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