Data-Driven Fault Detection for Industrial Processes (PDF)
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...
sofort als Download lieferbar
eBook (pdf)
80.24 €
40 DeutschlandCard Punkte
sammeln
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenloser tolino webreader
Produktdetails
Produktinformationen zu „Data-Driven Fault Detection for Industrial Processes (PDF)“
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.
Contents
- 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
Target Groups
- Researchers and students in the field of process control and statistical hypothesis testing
- Research and development engineers in the process industry
About the Author
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.
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, 112 Seiten, Englisch
- Verlag: Gabler, Betriebswirt.-Vlg
- ISBN-10: 3658167564
- ISBN-13: 9783658167561
- Erscheinungsdatum: 02.01.2017
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: PDF
- Größe: 4.96 MB
- Ohne Kopierschutz
- Vorlesefunktion
Sprache:
Englisch
Kommentar zu "Data-Driven Fault Detection for Industrial Processes"
0 Gebrauchte Artikel zu „Data-Driven Fault Detection for Industrial Processes“
| Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
|---|
Schreiben Sie einen Kommentar zu "Data-Driven Fault Detection for Industrial Processes".
Kommentar verfassen