Bearing Fault Diagnosis Approach under Data Quality Issues

In rotary machinery, bearings are susceptible to different types of mechanical faults, including ball, inner race, and outer race faults. condition-based monitoring (CBM), several techniques have been proposed in fault diagnostics based on the vibration measurements. For this paper, we studied fractal characteristics non-stationary signals collected from under health conditions. Using detrended fluctuation analysis (DFA), a novel method diagnose bearing faults scaling exponent (α1) signal at short-time scale. data with high sampling rate, our results showed that measure, exponent, provides an accurate identification state bearing. At end, evaluated performance quality issues, loss induced noise.