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An application of nonlinear feature extraction – a case study for low speed slewing bearing condition monitoring and prognosis

Conference Paper


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Abstract


  • This paper presents the application of four

    nonlinear methods of feature extraction in slewing bearing

    condition monitoring and prognosis: these are largest

    Lyapunov exponent, fractal dimension, correlation dimension,

    and approximate entropy methods. Although correlation

    dimension and approximate entropy methods have been used

    previously, the largest Lyapunov exponent and fractal

    dimension methods have not been used in vibration condition

    monitoring to date. The vibration data of the laboratory

    slewing bearing test-rig run at 1 rpm was acquired daily from

    February to August 2007 (138 days). As time progressed, a

    more accurate observation of the alteration of bearing

    condition from normal to faulty was obtained using nonlinear

    features extraction. These findings suggest that these methods

    provide superior descriptive information about bearing

    condition than time-domain features extraction, such as root

    mean square (RMS), variance, skewness and kurtosis.

Publication Date


  • 2013

Citation


  • Caesarendra, W., Kosasih, P., Tieu, A. K. & Moodie, C. (2013). An application of nonlinear feature extraction – a case study for low speed slewing bearing condition monitoring and prognosis. 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 1713-1718). United States: IEEE.

Scopus Eid


  • 2-s2.0-84883684262

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2276&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1267

Has Global Citation Frequency


Start Page


  • 1713

End Page


  • 1718

Place Of Publication


  • United States

Abstract


  • This paper presents the application of four

    nonlinear methods of feature extraction in slewing bearing

    condition monitoring and prognosis: these are largest

    Lyapunov exponent, fractal dimension, correlation dimension,

    and approximate entropy methods. Although correlation

    dimension and approximate entropy methods have been used

    previously, the largest Lyapunov exponent and fractal

    dimension methods have not been used in vibration condition

    monitoring to date. The vibration data of the laboratory

    slewing bearing test-rig run at 1 rpm was acquired daily from

    February to August 2007 (138 days). As time progressed, a

    more accurate observation of the alteration of bearing

    condition from normal to faulty was obtained using nonlinear

    features extraction. These findings suggest that these methods

    provide superior descriptive information about bearing

    condition than time-domain features extraction, such as root

    mean square (RMS), variance, skewness and kurtosis.

Publication Date


  • 2013

Citation


  • Caesarendra, W., Kosasih, P., Tieu, A. K. & Moodie, C. (2013). An application of nonlinear feature extraction – a case study for low speed slewing bearing condition monitoring and prognosis. 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 1713-1718). United States: IEEE.

Scopus Eid


  • 2-s2.0-84883684262

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2276&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1267

Has Global Citation Frequency


Start Page


  • 1713

End Page


  • 1718

Place Of Publication


  • United States