Machinery fault diagnosis by wavelet analysis

Many machines generate nonstationary dynamic signals. The featured components of such signals, such as spikes and transients, are usually localized both in time and in frequency. Since these features often carry rich information about the condition of the machines, enhancing and extracting them are...

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Bibliographic Details
Main Author:	Liu, Bao.
Other Authors:	Ling, Shih Fu
Format:	Theses and Dissertations
Published:	2008
Subjects:	DRNTU::Engineering::Mechanical engineering::Machine design and construction
Online Access:	http://hdl.handle.net/10356/6049
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Institution:	Nanyang Technological University

id	sg-ntu-dr.10356-6049
record_format	dspace
spelling	sg-ntu-dr.10356-60492023-03-11T16:55:16Z Machinery fault diagnosis by wavelet analysis Liu, Bao. Ling, Shih Fu School of Mechanical and Production Engineering DRNTU::Engineering::Mechanical engineering::Machine design and construction Many machines generate nonstationary dynamic signals. The featured components of such signals, such as spikes and transients, are usually localized both in time and in frequency. Since these features often carry rich information about the condition of the machines, enhancing and extracting them are of particular importance in machinery fault diagnosis. Doctor of Philosophy (MPE) 2008-09-17T11:05:37Z 2008-09-17T11:05:37Z 2000 2000 Thesis http://hdl.handle.net/10356/6049 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::Mechanical engineering::Machine design and construction
spellingShingle	DRNTU::Engineering::Mechanical engineering::Machine design and construction Liu, Bao. Machinery fault diagnosis by wavelet analysis
description	Many machines generate nonstationary dynamic signals. The featured components of such signals, such as spikes and transients, are usually localized both in time and in frequency. Since these features often carry rich information about the condition of the machines, enhancing and extracting them are of particular importance in machinery fault diagnosis.
author2	Ling, Shih Fu
author_facet	Ling, Shih Fu Liu, Bao.
format	Theses and Dissertations
author	Liu, Bao.
author_sort	Liu, Bao.
title	Machinery fault diagnosis by wavelet analysis
title_short	Machinery fault diagnosis by wavelet analysis
title_full	Machinery fault diagnosis by wavelet analysis
title_fullStr	Machinery fault diagnosis by wavelet analysis
title_full_unstemmed	Machinery fault diagnosis by wavelet analysis
title_sort	machinery fault diagnosis by wavelet analysis
publishDate	2008
url	http://hdl.handle.net/10356/6049
_version_	1761781287898578944

Machinery fault diagnosis by wavelet analysis

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