Multi-feature signature analysis for bearing condition monitoring using neural network methodology

Bibliographic Details
Main Author:	Cease, Barry T.
Published:	Georgia Institute of Technology 2008
Subjects:	Vibration Neural networks (Computer science) Artificial intelligence
Online Access:	http://hdl.handle.net/1853/19328

id	ndltd-GATECH-oai-smartech.gatech.edu-1853-19328
record_format	oai_dc
spelling	ndltd-GATECH-oai-smartech.gatech.edu-1853-193282013-12-15T03:37:24ZMulti-feature signature analysis for bearing condition monitoring using neural network methodologyCease, Barry T.VibrationNeural networks (Computer science)Artificial intelligenceGeorgia Institute of Technology2008-01-24T12:33:59Z2008-01-24T12:33:59Z1992-12Thesishttp://hdl.handle.net/1853/19328364230Access restricted to authorized Georgia Tech users only.
collection	NDLTD
sources	NDLTD
topic	Vibration Neural networks (Computer science) Artificial intelligence
spellingShingle	Vibration Neural networks (Computer science) Artificial intelligence Cease, Barry T. Multi-feature signature analysis for bearing condition monitoring using neural network methodology
author	Cease, Barry T.
author_facet	Cease, Barry T.
author_sort	Cease, Barry T.
title	Multi-feature signature analysis for bearing condition monitoring using neural network methodology
title_short	Multi-feature signature analysis for bearing condition monitoring using neural network methodology
title_full	Multi-feature signature analysis for bearing condition monitoring using neural network methodology
title_fullStr	Multi-feature signature analysis for bearing condition monitoring using neural network methodology
title_full_unstemmed	Multi-feature signature analysis for bearing condition monitoring using neural network methodology
title_sort	multi-feature signature analysis for bearing condition monitoring using neural network methodology
publisher	Georgia Institute of Technology
publishDate	2008
url	http://hdl.handle.net/1853/19328
work_keys_str_mv	AT ceasebarryt multifeaturesignatureanalysisforbearingconditionmonitoringusingneuralnetworkmethodology
_version_	1716618560670793728

Multi-feature signature analysis for bearing condition monitoring using neural network methodology

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