Abstract
In the condition based maintenance work of rotating machineries, bearings’ fault diagnosis and prognosis are an important content. Their faults can lead to many disasters. So, to detect the bearing faults earlier can benefit the remaining useful life (RUL) prediction and maintenance actions. In order to achieve this goal, narrowband interference cancellation (NIC) is used to extract the periodic impulsive signals which are indicative of a bearing fault. This method filters the narrowband signals not associated with the impulsive signal produced by bearing faults out. Therefore, the signal-to-noise will be improved and lead to the easier fault detection. However, only easier fault detection is not enough for RUL prediction. The features extracted from the vibration signals should reflect the bearings’ degradation well. This needs the features have good degradation trend (increase or decrease with time). In order to demonstrate the effectiveness of the proposed method, one implemented bearing fault test and one run-to-failure test are used to do the analysis. The results shows that bearing faults detection can be enhanced and root mean square (RMS) extracted from the NIC signal can track the bearing degradation well than the RMS extracted from the original vibration signal.
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This project is supported in part by the Chinese government research program under grant no. 51327020304.
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Zhang, X., Kang, J., Bechhoefer, E. et al. Enhanced bearing fault detection and degradation analysis based on narrowband interference cancellation. Int J Syst Assur Eng Manag 5, 645–650 (2014). https://doi.org/10.1007/s13198-014-0217-6
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DOI: https://doi.org/10.1007/s13198-014-0217-6