Feature extraction of the hydraulic pump fault based on improved Autogram

Abstract

Abstract Autogram is a good tool to extract the fault feature information from a fault vibration signal of rolling element bearings. Structure of a hydraulic pump is more complicated than the rolling element bearings, and its vibration signal is more contaminated by heavy Gaussian and non-Gaussian noises if slipper wear fault happens, and too many noise amplitudes can be introduced into computation of kurtosis in the time domain, and a data source of containing rich fault feature information cannot be successfully selected by the kurtosis, and it is ineffective application of the hydraulic pump. Aiming to resolve the above problems, an improved Autogram called PSE-Autogram is proposed. Its key and different selection of the data source is completed by the power spectral entropy (PSE) rather than the kurtosis, and the fault feature information can be made highlighted and noise can be suppressed by PSE in the frequency domain, and shortcomings of the original Autogram can be effectively overcame. A simulated signal and a slipper wear fault signal are tested and verified, and results demonstrate that PSE-Autogram performs better than Autogram and traditional fast kurtogram based on the assessment criterion of feature energy ratio.