Fault Isolation Using a Reconstruction Algorithm

Name: Fault Isolation Using a Reconstruction Algorithm
Brand: LAP Lambert Academic Publishing
SKU: 9783659323843
Price: 254.35 RON
Availability: InStock

By Using Auto-Associative Neural Networks

Autor: Sayyed Hamidreza Mousavi, Mehdi Shahbazian

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Editura: LAP Lambert Academic Publishing

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

Informații despre carte

Autor

Sayyed Hamidreza Mousavi, Mehdi Shahbazian

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2013

Pagini

EAN

9783659323843

Enbook ID

06957689

Editura

LAP Lambert Academic Publishing

Greutate

147

Dimensiuni

150 x 220 x 5

Descriere completă

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

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