Digitální knihovnaUPCE
 

Fault severity detection of ball bearings and efficiency of one-period analysis in early fault diagnosis of rotating machinery

Konferenční objektOmezený přístuppeer-reviewedpublished version
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Datum publikování

2016

Vedoucí práce

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Název svazku

Vydavatel

JVE International

Abstrakt

This paper investigates several number of methods: Wavelet Packet Energy (WPE), Time-domain features and Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) which are efficient of extracting features in fault diagnosis of rotating machinery. The database, which is attained via Bearing Data Center of Case Western Reserve University (CWRU), includes signal samples related to the different faulty cases and severity levels of bearing type 6205-2RS JEM. Throughout the research, combination of different faulty sample signals which are segmented into different number of periods, one of which is so called one-period analysis, of rotation of the motor are used in order to classify early faults of bearings and five class severity levels of ball bearings. Upon using proposed approaches, an outstanding classification performance of 100% and 99,7% are observed in specificity of early faults by the use of one-period analysis and five severity level classification of ball faults, respectively.

Rozsah stran

p. 76-81

ISSN

2345-0533

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Projekt

Zdrojový dokument

Vibroengineering Procedia

Vydavatelská verze

http://www.jvejournals.com/Vibro/article/VP-17430.html

Přístup k e-verzi

Pouze v rámci univerzity

Název akce

21st International Conference on Vibroengineering (31.08.2016 - 01.09.2016)

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Klíčová slova

wavelet packet energy, multipoint optimal minimum entropy deconvolution, bearing fault diagnosis, one period analysis, support vector machine, Fisher linear discriminant analysis, diagnostika ložisek, wavelet packet energy, multipoint optimal minimum entropy deconvolution, one period analysis, support vector machine, Fisher linear discriminant analysis

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