Abstract
The ability to detect and diagnose faults in rolling element bearings is crucial for modern maintenance schemes. Several techniques have been developed to improve the ability of fault detection in bearings using vibration monitoring, especially in those cases where the vibration signal is contaminated by background noise. Linear prediction (LP) and self-adaptive noise cancellation (SANC) are techniques which can substantially improve the signal to noise ratio of a signal, improving the visibility of the important signal components in the frequency spectrum. Spectral kurtosis (SK) has been shown to improve bearing defect identification by focusing on the frequency band with higher level of impulsiveness. In this paper, the ability of these three methods to detect a bearing fault is compared, using vibrational data from a specially designed test rig that allowed fast natural degradation of the bearing. The results obtained show that the SK was able to detect an incipient fault in the outer race of the bearing much earlier than any other technique.
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Acknowledgments
Financial support from the Marie Curie FP7-ITN project “Energy savings from smart operation of electrical, process and mechanical equipment—ENERGY-SMARTOPS,” Contract No: PITN-GA-2010-264940 is gratefully acknowledged.
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Ruiz-Cárcel, C., Hernani-Ros, E., Chandra, P., Cao, Y., Mba, D. (2015). Application of Linear Prediction, Self-Adaptive Noise Cancellation, and Spectral Kurtosis in Identifying Natural Damage of Rolling Element Bearing in a Gearbox. In: Lee, W., Choi, B., Ma, L., Mathew, J. (eds) Proceedings of the 7th World Congress on Engineering Asset Management (WCEAM 2012). Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-06966-1_45
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DOI: https://doi.org/10.1007/978-3-319-06966-1_45
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