Data-Driven Fault Detection for Industrial Processes
Canonical Correlation Analysis and Projection Based Methods
(Sprache: Englisch)
Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the...
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Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed.Inhaltsverzeichnis zu „Data-Driven Fault Detection for Industrial Processes “
A New Index for Performance Evaluation of FD Methods.- CCA-based FD Method for the Monitoring of Stationary Processes.- Projection-based FD Method for the Monitoring of Dynamic Processes.- Benchmark Study and Real-Time Implementation.Autoren-Porträt von Zhiwen Chen
Zhiwen Chen's research interests include multivariate statistical process monitoring, model-based and data-driven fault diagnosis as well as their application to industrial processes. He is currently working at the School of Information Science and Engineering at Central South University, China.Bibliographische Angaben
- Autor: Zhiwen Chen
- 2017, 1st ed. 2017, XIX, 112 Seiten, Masse: 14,9 x 21,6 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3658167556
- ISBN-13: 9783658167554
- Erscheinungsdatum: 27.01.2017
Sprache:
Englisch
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