Kai Zhang
Orcid: 0000-0002-3708-8945Affiliations:
- University of Science and Technology of Beijing, School of Automation and Electrical Engineering, China
- University of Duisburg-Essen, Institute for Automatic Control and Complex Systems, Duisburg, Germany (PhD 2016)
According to our database1,
Kai Zhang
authored at least 24 papers
between 2013 and 2021.
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Bibliography
2021
A Novel Feature-Extraction-Based Process Monitoring Method for Multimode Processes With Common Features and Its Applications to a Rolling Process.
IEEE Trans. Ind. Informatics, 2021
2020
A Correlation-Based Distributed Fault Detection Method and Its Application to a Hot Tandem Rolling Mill Process.
IEEE Trans. Ind. Electron., 2020
Monitoring of Nonlinear Processes With Multiple Operating Modes Through a Novel Gaussian Mixture Variational Autoencoder Model.
IEEE Access, 2020
2019
IEEE Trans. Syst. Man Cybern. Syst., 2019
A Distributed Canonical Correlation Analysis-Based Fault Detection Method for Plant-Wide Process Monitoring.
IEEE Trans. Ind. Informatics, 2019
A Kernel Canonical Correlation Analysis-Based Fault Detection Method with Application to a Hot Tandem Rolling Mill Process.
Proceedings of the CAA Symposium on Fault Detection, 2019
A Novel Scheme for Remaining Useful Life Prediction and Safety Assessment Based on Hybrid Method.
Proceedings of the CAA Symposium on Fault Detection, 2019
2018
A Common and Individual Feature Extraction-Based Multimode Process Monitoring Method With Application to the Finishing Mill Process.
IEEE Trans. Ind. Informatics, 2018
Implementing multivariate statistics-based process monitoring: A comparison of basic data modeling approaches.
Neurocomputing, 2018
A Comparison of Different Statistics for Detecting Multiplicative Faults in Multivariate Statistics-Based Fault Detection Approaches.
IEEE Access, 2018
2017
Assessment of <i>T</i><sup>2</sup>- and <i>Q</i>-statistics for detecting additive and multiplicative faults in multivariate statistical process monitoring.
J. Frankl. Inst., 2017
A novel dynamic non-Gaussian approach for quality-related fault diagnosis with application to the hot strip mill process.
J. Frankl. Inst., 2017
An alternative data-driven fault detection scheme for dynamic processes with deterministic disturbances.
J. Frankl. Inst., 2017
2016
A Quality-Based Nonlinear Fault Diagnosis Framework Focusing on Industrial Multimode Batch Processes.
IEEE Trans. Ind. Electron., 2016
Quality-related process monitoring for dynamic non-Gaussian batch process with multi-phase using a new data-driven method.
Neurocomputing, 2016
A brief survey of different statistics for detecting multiplicative faults in multivariate statistical process monitoring.
Proceedings of the 55th IEEE Conference on Decision and Control, 2016
Springer Vieweg, ISBN: 978-3-658-15971-9, 2016
2015
Quality-relevant fault detection and diagnosis for hot strip mill process with multi-specification and multi-batch measurements.
J. Frankl. Inst., 2015
Quality-related prediction and monitoring of multi-mode processes using multiple PLS with application to an industrial hot strip mill.
Neurocomputing, 2015
Adaptive total PLS based quality-relevant process monitoring with application to the Tennessee Eastman process.
Neurocomputing, 2015
2014
A new data-driven process monitoring scheme for key performance indictors with application to hot strip mill process.
J. Frankl. Inst., 2014
A data-driven fault detection approach for static processes with deterministic disturbances.
Proceedings of the 23rd IEEE International Symposium on Industrial Electronics, 2014
Proceedings of the 2014 IEEE Conference on Control Applications, 2014
2013
Proceedings of the 10th IEEE International Conference on Control and Automation, 2013