Tacholess bearing fault detection based on adaptive impulse extraction in the time domain under fluctuant speed

The bearing diagnosis in industrial applications is limited because the traditional equal-time sampling strategy results in the loss of signal period and fault feature under fluctuant speed. To address these issues, a new tacholess bearing fault diagnosis method based on time-domain impulse extraction (TDIE) is proposed. With the TDIE method, the frequency fluctuant phenomenon is eliminated and fault characteristic frequencies are exposed distinctly. In contrast to traditional time-frequency analysis techniques, straightforward phase information is analyzed in the time-domain with the new adaptive morphological filtering and identified impulse optimization methods. Finally, angular resampling is employed to realize bearing fault diagnosis under fluctuant speed. To demonstrate the performance of the proposed method, simulation and experiments are conducted. Results indicate that the technique is an effective tacholess bearing fault detection under fluctuant speed with enhanced efficiency and robustness.