Journal
JOURNAL OF SOUND AND VIBRATION
Volume 332, Issue 25, Pages 6871-6890Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jsv.2013.07.021
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Funding
- Research Grants Council of the Hong Kong Special Administrative Region, China [CityU 122011]
- City University of Hong Kong [7008187]
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Rolling element bearings are widely used in rotating machinery. Its unexpected failure may result in machine breakdown. Whenever a bearing suffers a localized fault, the transients with a potential cyclic characteristic are generated by the rollers striking the localized fault. This phenomenon is an early bearing fault feature. Therefore, the extraction of the transients is beneficial to the identification of the early bearing fault. In this paper, a novel adaptive wavelet stripping algorithm (AWSA) is proposed to extract the simulated transients from an original bearing fault signal. Firstly, the parametric model of anti-symmetric real Laplace wavelet (ARLW) or impulse response wavelet (IRW) is built to approximate the real transients. Then, with the aid of wavelet correlation filtering analysis, the simulated transients with the optimal frequency, damping coefficient and delay time are adaptively peeled from the original bearing fault signal. The spatial reconstruction of the simulated transients reflects the random occurrence of the real transients. In order to boost the computing time of the AWSA, an enhanced AWSA is developed. At last, the bearing fault signals collected from an experimental machine and an industrial machine are used to validate the effectiveness of the AWSA. The results show that the AWSA can adaptively peel the simulated transients from the original bearing fault signals. A comparison with a periodic multi-transient model is conducted to show that the AWSA is better to extract the random characteristics of the real transients. (C) 2013 Elsevier Ltd. All rights reserved.
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