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

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 en...

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Bibliographic Details
Published in2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics pp. 1713 - 1718
Main Authors Caesarendra, Wahyu, Kosasih, Buyung, Kiet Tieu, Moodie, Craig A. S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2013
Subjects
Correlation
Entropy
Feature extraction
Fractals
Time-domain analysis
Vectors
Vibrations
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ISBN1467353191
9781467353199
ISSN2159-6247
DOI10.1109/AIM.2013.6584344

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Summary: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.
ISBN:1467353191
9781467353199
ISSN:2159-6247
DOI:10.1109/AIM.2013.6584344