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(Ebook) Data-Driven Fault Detection for Industrial Processes: Canonical Correlation Analysis and Projection Based Methods by Zhiwen Chen (auth.) ISBN 9783658167554, 9783658167561, 3658167556, 3658167564

  • SKU: EBN-5885144
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Instant download (eBook) Data-Driven Fault Detection for Industrial Processes: Canonical Correlation Analysis and Projection Based Methods after payment.
Authors:Zhiwen Chen (auth.)
Pages:124 pages.
Year:2017
Editon:1
Publisher:Springer Vieweg
Language:english
File Size:3.85 MB
Format:pdf
ISBNS:9783658167554, 9783658167561, 3658167556, 3658167564
Categories: Ebooks

Product desciption

(Ebook) Data-Driven Fault Detection for Industrial Processes: Canonical Correlation Analysis and Projection Based Methods by Zhiwen Chen (auth.) ISBN 9783658167554, 9783658167561, 3658167556, 3658167564

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.
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