PREDICTIVE METHODS TO ANALYSE BEARING FAILURE USING LAB VIEW AND DAQ TECHNIQUE

Mechanical

In any rotating machinery rolling element bearings are one of the vital components. Are found in industries from agriculture to aerospace, the failure of the bearings will lead to complete shutdown or malfunction of the machinery. This results in economic losses and substantial time .Before the catastrophic consequences it is important to know the health of the bearings and the severity of the defect. This work describes the suitability of the statistical parameters such as kurtosis, crest factor, peak and RMS as the techniques for monitoring defects. The work also describes effect of the speed and the defect size on these parameters. The fundamentals of rolling element bearings are presented in line with different failure modes and relevant monitoring methods in the time domain, the frequency domain, the envelope spectrum and the wavelet analysis. These reviews show that vibration measurements are a proven and widely accepted data source for bearing monitoring of machinery. For the present study, the faults in the bearings are created artificially using Electric Discharge Machine (EDM). Some advantages of standard and portable DAQ Equipment with LabVIEW software also discussed.

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