Novel Technology Based on the Spectral Kurtosis and Wavelet Transform for Rolling Bearing Diagnosis

Len Gelman, Tejas H. Patel, Gabrijel Persin, Brian Murray, Allan Thomson

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

A novel diagnosis technology combining the benefits of spectral kurtosis and wavelet transform is proposed and validated for early defect diagnosis of rolling element bearings. A systematic procedure for feature calculation is proposed and rules for selection of technology parameters are explained. Experimental validation of the proposed method carried out for early detection of the inner race defect. A comparison between frequency band selection through wavelets and spectral kurtosis is also presented. It has been observed that the frequency band selected using spectral kurtosis provide better separation between healthy and defective bearings compared to the frequency band selection using wavelet. In terms of Fisher criterion the use of spectral kurtosis has a gain of 2.75 times compared to the wavelet.

Original languageEnglish
Article number 024
Number of pages7
JournalInternational Journal of Prognostics and Health Management
Volume4
Issue number2
Publication statusPublished - 2013
Externally publishedYes

Fingerprint Dive into the research topics of 'Novel Technology Based on the Spectral Kurtosis and Wavelet Transform for Rolling Bearing Diagnosis'. Together they form a unique fingerprint.

Cite this