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Application of the largest Lyapunov exponent algorithm for feature extraction in low speed slew bearing condition monitoring

Journal Article


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Abstract


  • This paper presents a new application of the largest Lyapunov exponent (LLE) algorithm for feature extraction method in low speed slew bearing condition monitoring. The LLE algorithm is employed to measure the degree of non-linearity of the vibration signal which is not easily monitored by existing methods. The method is able to detect changes in the condition of the bearing and demonstrates better tracking of the progressive deterioration of the bearing during the 139 measurement days than comparable methods such as the time domain feature methods based on root mean square (RMS), skewness and kurtosis extraction from the raw vibration signal and also better than extracting similar features from selected intrinsic mode functions (IMFs) of the empirical mode decomposition (EMD) result. The application of the method is demonstrated with laboratory slew bearing vibration data and industrial bearing data from a coal bridge reclaimer used in a local steel mill.

Publication Date


  • 2015

Citation


  • Caesarendra, W., Kosasih, B., Tieu, A. Kiet . & Moodie, C. A. S. (2015). Application of the largest Lyapunov exponent algorithm for feature extraction in low speed slew bearing condition monitoring. Mechanical Systems and Signal Processing, 50-51 116-138.

Scopus Eid


  • 2-s2.0-84905870029

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3914&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2905

Has Global Citation Frequency


Number Of Pages


  • 22

Start Page


  • 116

End Page


  • 138

Volume


  • 50-51

Place Of Publication


  • United Kingdom

Abstract


  • This paper presents a new application of the largest Lyapunov exponent (LLE) algorithm for feature extraction method in low speed slew bearing condition monitoring. The LLE algorithm is employed to measure the degree of non-linearity of the vibration signal which is not easily monitored by existing methods. The method is able to detect changes in the condition of the bearing and demonstrates better tracking of the progressive deterioration of the bearing during the 139 measurement days than comparable methods such as the time domain feature methods based on root mean square (RMS), skewness and kurtosis extraction from the raw vibration signal and also better than extracting similar features from selected intrinsic mode functions (IMFs) of the empirical mode decomposition (EMD) result. The application of the method is demonstrated with laboratory slew bearing vibration data and industrial bearing data from a coal bridge reclaimer used in a local steel mill.

Publication Date


  • 2015

Citation


  • Caesarendra, W., Kosasih, B., Tieu, A. Kiet . & Moodie, C. A. S. (2015). Application of the largest Lyapunov exponent algorithm for feature extraction in low speed slew bearing condition monitoring. Mechanical Systems and Signal Processing, 50-51 116-138.

Scopus Eid


  • 2-s2.0-84905870029

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3914&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2905

Has Global Citation Frequency


Number Of Pages


  • 22

Start Page


  • 116

End Page


  • 138

Volume


  • 50-51

Place Of Publication


  • United Kingdom