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Communication Dans Un Congrès Année : 2021

Detection of balls bearing Incipient defect by current analysis


The bearing problem is considered as the first factor responsible of defects in the electric machines. Traditionally, rolling monitoring is based on the analysis of vibration signals requiring sensors around the machine. The objective of our research work is based on the analysis of electromagnetic signals (current, voltage, stray flux) to detect faults arising in the different bearing parts (inner raceway, outer raceway, rolling elements (balls), cage). The simple model of balls bearings coupled to the machine model has been developed and implemented with Simulink/Matlab to simulate the fault in the bearing. The simulations are exploited from a mechanical point of view (torque, vibrations) and from an electromagnetic point of view (current, flux, voltage). Fast Fourier Transforms analysis was applied to the mechanical and electromagnetic signals, to extract the specific frequencies of each fault. The low amplitudes of the signal characteristics, for incipient faults, are often hidden by background interferences (Signal to Noise Ratio, SNR). The methods of envelope and of background noises frequencies cancellation have been employed to overcome this problem.
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Dates et versions

hal-03654214 , version 1 (28-04-2022)



Amine Yazidi, Humberto Henao, Franck Betin, Eric Segard, Hassan Gaditto Mahamad, et al.. Detection of balls bearing Incipient defect by current analysis. IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, Oct 2021, Toronto, Canada. ⟨10.1109/IECON48115.2021.9589545⟩. ⟨hal-03654214⟩


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