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Fault Isolation Using a Reconstruction Algorithm

By Using Auto-Associative Neural Networks

Language EnglishEnglish
Book Paperback
Book Fault Isolation Using a Reconstruction Algorithm Sayyed Hamidreza Mousavi
Libristo code: 06957689
Publishers LAP Lambert Academic Publishing, November 2012
Process history based approaches for fault diagnosis has been widely used recently. Principal Compon... Full description
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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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About the book

Full name Fault Isolation Using a Reconstruction Algorithm
Language English
Binding Book - Paperback
Date of issue 2013
Number of pages 88
EAN 9783659323843
Libristo code 06957689
Weight 147
Dimensions 150 x 220 x 5
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