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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 190-191Feature Extraction of Rolling Bearing Fault...

Feature Extraction of Rolling Bearing Fault Diagnosis

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Abstract:

Mechanical equipment fault diagnosis occupies an important position in the industrial production, and feature extraction plays an important role in fault diagnosis. This paper analyzes various methods of feature extraction in rolling bearing fault diagnosis and classifies them into two big categories, which are methods of depending on empirical rules and experimental trials and using objective methods for screening. The former includes five methods: frequency as the characteristic parameters, multi-sensor information fusion method, rough set attribute reduction method, "zoom" method and vibration signal as the characteristic parameters. The latter includes two methods: sensitivity extraction and data mining methods to select attributes. Currently, selection methods of feature parameters depend heavily on empirical rules and experimental trials, thus extraction results are be subjected to restriction from subjective level, feature extraction in the future will develop toward objective screening direction.

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Digital Manufacturing & Automation III

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Periodical:

Applied Mechanics and Materials (Volumes 190-191)

Pages:

993-997

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.190-191.993

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Online since:

July 2012

Authors:

Li Jie Sun, Li Zhang, Yong Bo Yang, Da Bo Zhang, Li Chun Wu

Keywords:

Fault Diagnosis, Feature Extraction, Rolling Bearing

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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