Fault diagnosis of rolling bearings based on impulse feature enhancement and time-frequency joint noise reduction

Aiming at the problem that the effectiveness of impulse feature enhancement (IFE) depends on the duration of high-level (or low-level) K and the number of high-level L , we regard a segmented impulse norm as the fitness function and combine it with the whale optimization algorithm to select the optimal parameters adaptively. Time-frequency joint noise reduction (TFJNR) is also proposed to suppress the noise components in the signal. Simulation and experimental results of rolling bearings indicate that the proposed algorithm can rapidly select the optimal parameters K and L to ensure the performance of IFE, while TFJNR has the ability to suppress the noise components in the signal. Fast kurtogram, empirical mode decomposition, and fast spectral correlation are also used for comparison. The results highlight the performance of the proposed algorithm.