Fault Isolation Using a Reconstruction Algorithm

Name: Fault Isolation Using a Reconstruction Algorithm
Brand: LAP Lambert Academic Publishing
SKU: 9783659323843
Price: 1173 CZK
Availability: InStock

By Using Auto-Associative Neural Networks

Autor: Sayyed Hamidreza Mousavi, Mehdi Shahbazian

Jazyk:

Vazba: Brožovaná

Vydavatel: LAP Lambert Academic Publishing

Dostupnost: U nakladatele na objednávku

Odesíláme za 17-27 dnů

1 173 Kč

Process history based approaches for fault diagnosis has been widely used recently. Principal Compon...

Informace o knize

Autor

Sayyed Hamidreza Mousavi, Mehdi Shahbazian

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2013

Stránek

EAN

9783659323843

Enbook ID

06957689

Vydavatel

LAP Lambert Academic Publishing

Hmotnost

147

Rozměry

150 x 220 x 5

Kompletní popis

Process history based approaches for fault diagnosis has been widely used recently. Principal Component Analysis (PCA) is one of these approaches, which is a linear approach; however most of the processes are nonlinear. Hence nonlinear extensions of the PCA have been developed. Nonlinear Principal Component Analysis (NLPCA) based on the neural networks is a common method which is used for process monitoring and fault diagnosis. NLPCA based neural networks are implemented using different methods, in this book we apply Auto-Associative Neural Networks (AANN) for implementing NLPCA. This work is aimed towards the development of an algorithm used in conjunction with an Auto Associative Neural Network (AANN) to help locate and reconstruct faulty sensor inputs in control systems. Also an algorithm is developed for locating the source of the process fault.

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Fault Isolation Using a Reconstruction Algorithm

By Using Auto-Associative Neural Networks

Informace o knize

Kompletní popis

Mohlo by vás zajímat

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