Yuri A. W. Shardt
Yuri A. W. Shardt
Professor, TU Ilmenau
Verified email at tu-ilmenau.de - Homepage
Title
Cited by
Cited by
Year
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
812016
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
722012
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
582020
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
582014
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
372011
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
332017
Closed-loop identification condition for ARMAX models using routine operating data
YAW Shardt, B Huang
Automatica 47 (7), 1534-1537, 2011
332011
Data quality assessment of routine operating data for process identification
YAW Shardt, B Huang
Computers & chemical engineering 55, 19-27, 2013
302013
Statistics for chemical and process engineers
YAW Shardt
Springer International Publishing: Berlin, Germany, 2015
292015
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
272018
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
202017
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
192018
An adaptive, advanced control strategy for KPI-based optimization of industrial processes
S Dominic, YAW Shardt, SX Ding, H Luo
IEEE Transactions on Industrial Electronics 63 (5), 3252-3260, 2015
192015
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
182016
Using the expected detection delay to assess the performance of different multivariate statistical process monitoring methods for multiplicative and drift faults
K Zhang, YAW Shardt, Z Chen, K Peng
ISA transactions 67, 56-66, 2017
152017
Minimal required excitation for closed-loop identification: Some implications for data-driven, system identification
YAW Shardt, B Huang, SX Ding
Journal of Process Control 27, 22-35, 2015
152015
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
142020
Tuning a soft sensor’s bias update term. 1. The open-loop case
YAW Shardt, B Huang
Industrial & engineering chemistry research 51 (13), 4958-4967, 2012
142012
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
132020
Tuning a soft sensor’s bias update term. 2. the closed-loop case
YAW Shardt, B Huang
Industrial & engineering chemistry research 51 (13), 4968-4981, 2012
112012
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Articles 1–20