A hybrid method of frequency-weighted energy operator and power spectrum fusion to detect bearing faults.

The difficulties in early fault diagnosis of bearings mainly include two aspects: first, the initial damage size of the bearing is small, and the abnormal vibration caused by slight damage to the bearing is very weak. Second, vibration signals collected in actual industrial environments always contain strong noise interference. Therefore, traditional diagnostic procedures are not satisfactory. To address these challenges, this work provides a hybrid model combining frequency-weighted energy operator (FWEO) with power spectrum fusion (PSF) to identify weak fault features of bearings and detect different fault types. Different from traditional time-domain signal filtering, PSF is first used to reduce the interference of noise components in the power spectrum, which will not weaken the fault signal components during denoising. Second, the filtered signal is transformed into the time domain and FWEO is employed to further enhance the cyclic fault signal caused by the weak defect of the bearing. Finally, the existence of a fault is identified by observing the squared envelope spectrum of the signal. The effectiveness of the proposed hybrid model is demonstrated through two simulated fault signals and three different experimental fault signals. The results show that the proposed model has high anti-noise performance and robustness and can extract the fault frequency well.