Fuzzy entropy assisted singular spectrum decomposition to detect bearing faults in axial piston pump

Abstract The fault feature of vibration signal is always submerged in heavy interference noise and strong pumping impulses in axial piston pump bearings. A fuzzy entropy assisted singular spectrum decomposition denoising method is introduced to extract bearing fault in axial piston pump. The raw measured data is adaptively decomposed to singular spectrum components. The fuzzy entropy of singular spectrum components is used to screen the component with critical information and remove the unrelated components. The remaining singular spectrum components are conducted for the secondary screening to decrease impulse interference based on the prior knowledge. Simulation results of signals with different noise intensity thoroughly validate performance. This method is applied to process practical engineering signals and the characteristic frequency intensity coefficient is taken as the evaluation index in the field. The experimental results show that the evaluation index of the presented technique are increased by 80%, 56% and 40%, respectively.