Abstract
By use of the custom-built crane model, loads, acoustic emission instrument, and sensor, AE tests for the slewing bearing with no defect, the slewing bearing with outer race defect, and the slewing bearing with roller defect had been conducted under the operating conditions of low speed and heavy duty. A lot of AE signals had been acquired and processed. AE signal acquired under different working conditions had made wavelet decomposition using Daubechies wavelet. All wavelet energy spectrum coefficients under every working condition had been calculated. The judgment basis for the slewing bearing fault had been obtained by the distribution law of wavelet energy spectrum coefficients under different working conditions. This study lays a theoretical foundation for the fault diagnosis of crane slewing bearing working at low speed and heavy duty.
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Acknowledgment
The research work was supported by National Key Technology Support Program No. 2011BAK06B03-03.
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Jiao, Y., Li, G., Wu, Z., Geng, H., Zhang, J., Cheng, L. (2017). Fault Analysis for Low-Speed Heavy-Duty Crane Slewing Bearing Based on Wavelet Energy Spectrum Coefficient. In: Shen, G., Wu, Z., Zhang, J. (eds) Advances in Acoustic Emission Technology. Springer Proceedings in Physics, vol 179. Springer, Cham. https://doi.org/10.1007/978-3-319-29052-2_6
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DOI: https://doi.org/10.1007/978-3-319-29052-2_6
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