期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 50-51, 期 -, 页码 116-138出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2014.05.021
关键词
Empirical mode decomposition; Feature extraction; Largest Lyapunov exponent; Low speed slew bearing
资金
- University of Wollongong through University Postgraduate Award (UPA)
- International Postgraduate Tuition Award (IPTA)
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. (C) 2014 Elsevier Ltd. All rights reserved.
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