Fourier Harmonic Regression Method for Bearing Condition Monitoring using Vibration Measurements

Abstract Recently, condition monitoring and fault diagnosis of rolling element bearings in rotary machinery is an important task in industries for system preventive maintenance, increase reliability, improved productivity and increase machine availability. Existing methods for condition monitoring of bearings require extensive experimental arrangements using costly sensors and complex computational techniques in frequency domain for fault diagnosis. This work presents a simple time domain based approach using Fourier harmonic regression analysis method for condition monitoring of rolling element bearings. In the present work, bearing vibrations are acquired in normal and faulty conditions. A Fourier harmonic regression analysis method is applied to determine the confidence intervals for the bearing condition monitoring. The magnitude of harmonics and standard deviation of residuals are analyzed for identifying effect of faults like outer race fault, inner race fault and bearing ball fault. Experimental results show the effectiveness in detection of bearing conditions using the calculations in time domain.