Data-Driven Fault Detection for Industrial Processes - Canonical Correlation Analysis and Projection Based Methods (Paperback, 1st ed. 2017)

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.

General

Imprint:	Springer Vieweg
Country of origin:	Germany
Release date:	2017
First published:	2017
Authors:	Zhiwen Chen
Dimensions:	210 x 148 x 7mm (L x W x T)
Format:	Paperback
Pages:	112
Edition:	1st ed. 2017
ISBN-13:	978-3-658-16755-4
Categories:	Books > Professional & Technical > Industrial chemistry & manufacturing technologies > General Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Professional & Technical > Mechanical engineering & materials > Production engineering > Reliability engineering Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General
LSN:	3-658-16755-6
Barcode:	9783658167554

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