Fault detection and isolation using neural networks in structural dynamic systems

In general, there is a possibility of degradation in stiffness due to environmental loadings such as earthquakes, for multi-storey steel frame structures, which are designed with enough stiffness and strength. Study on the identification of failure of such systems and taking corrective actions will...

Full description

Saved in:

Bibliographic Details
Main Author:	Pandian Thirupura Sundari
Other Authors:	Sundararajan, Narasimhan
Format:	Theses and Dissertations
Published:	2008
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems DRNTU::Engineering::Computer science and engineering::Computing methodologies
Online Access:	http://hdl.handle.net/10356/5020
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Nanyang Technological University

id	sg-ntu-dr.10356-5020
record_format	dspace
spelling	sg-ntu-dr.10356-50202023-07-04T15:11:01Z Fault detection and isolation using neural networks in structural dynamic systems Pandian Thirupura Sundari Sundararajan, Narasimhan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems DRNTU::Engineering::Computer science and engineering::Computing methodologies In general, there is a possibility of degradation in stiffness due to environmental loadings such as earthquakes, for multi-storey steel frame structures, which are designed with enough stiffness and strength. Study on the identification of failure of such systems and taking corrective actions will go a long way in improving the safety of the systems. In this dissertation, the above structural dynamic system represented by a spring mass damper system (two masses) is considered here for the ease and understanding of solving this problem. Master of Science (Computer Control and Automation) 2008-09-17T10:03:26Z 2008-09-17T10:03:26Z 2006 2006 Thesis http://hdl.handle.net/10356/5020 Nanyang Technological University application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
topic	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems DRNTU::Engineering::Computer science and engineering::Computing methodologies
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems DRNTU::Engineering::Computer science and engineering::Computing methodologies Pandian Thirupura Sundari Fault detection and isolation using neural networks in structural dynamic systems
description	In general, there is a possibility of degradation in stiffness due to environmental loadings such as earthquakes, for multi-storey steel frame structures, which are designed with enough stiffness and strength. Study on the identification of failure of such systems and taking corrective actions will go a long way in improving the safety of the systems. In this dissertation, the above structural dynamic system represented by a spring mass damper system (two masses) is considered here for the ease and understanding of solving this problem.
author2	Sundararajan, Narasimhan
author_facet	Sundararajan, Narasimhan Pandian Thirupura Sundari
format	Theses and Dissertations
author	Pandian Thirupura Sundari
author_sort	Pandian Thirupura Sundari
title	Fault detection and isolation using neural networks in structural dynamic systems
title_short	Fault detection and isolation using neural networks in structural dynamic systems
title_full	Fault detection and isolation using neural networks in structural dynamic systems
title_fullStr	Fault detection and isolation using neural networks in structural dynamic systems
title_full_unstemmed	Fault detection and isolation using neural networks in structural dynamic systems
title_sort	fault detection and isolation using neural networks in structural dynamic systems
publishDate	2008
url	http://hdl.handle.net/10356/5020
_version_	1772825831538688000

Fault detection and isolation using neural networks in structural dynamic systems

Similar Items