Overview
- Publication in the field of technical sciences
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (8 chapters)
-
Front Matter
-
Back Matter
Keywords
About this book
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.
Authors and Affiliations
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.
Bibliographic Information
Book Title: Data-Driven Fault Detection for Industrial Processes
Book Subtitle: Canonical Correlation Analysis and Projection Based Methods
Authors: Zhiwen Chen
DOI: https://doi.org/10.1007/978-3-658-16756-1
Publisher: Springer Vieweg Wiesbaden
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Fachmedien Wiesbaden GmbH 2017
Softcover ISBN: 978-3-658-16755-4Published: 09 January 2017
eBook ISBN: 978-3-658-16756-1Published: 02 January 2017
Edition Number: 1
Number of Pages: XIX, 112
Number of Illustrations: 39 b/w illustrations
Topics: Control and Systems Theory, Mathematical and Computational Engineering