Fault Diagnosis of Rolling Bearing Based on Feature-Level Fusion Method

Ling Li Jiang; Hua Kui Yin; Si Wen Tang

doi:10.4028/www.scientific.net/AMM.273.260

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 273Fault Diagnosis of Rolling Bearing Based on...

Fault Diagnosis of Rolling Bearing Based on Feature-Level Fusion Method

Abstract:

Bearing failure is one of the foremost causes of breakdowns in rotating machinery and such failure can be catastrophic. Fault diagnosis is critical to maintaining the normal operation of the bearings. This paper proposes feature-level fusion method for rolling bearing fault diagnosis. Features are extracted from eight vibration signals to constitute a fusion vector. SVM is used for pattern recognition. The case study results show that the proposed method is useful for rolling bearing fault diagnosis.

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Periodical:

Applied Mechanics and Materials (Volume 273)

Pages:

260-263

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.273.260

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Online since:

January 2013

Authors:

Ling Li Jiang, Hua Kui Yin, Si Wen Tang

Keywords:

Fault Diagnosis, Feature-Level Fusion, Rolling Bearing, Support Vector Machine (SVM)

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