PPSE-FBE: A Piston Pump Fault Feature Extraction Method Assisted by Time Frequency Localization

This article proposes a time–frequency localization-assisted method for extracting fault features from piston pumps. This method can optimize the sample setting of intelligent fault diagnosis models, enhance diagnostic effectiveness, and reduce model complexity. First, ILTET-PPSE method is proposed, which integrates the improved local maximum transient extraction transform (ILTET) and periodic pulse screening and extraction (PPSE) methods. This approach enables precise localization, segmentation, extraction, and combination of vibration signals from a single piston. Subsequently, considering the nonlinear and nonstationary characteristics of the vibration signals, a novel fault feature extraction algorithm termed fractional-order bubble entropy (FBE) is proposed. Furthermore, a fault feature extraction method, PPSE-FBE, assisted by ILTET-PPSE, is introduced. PPSE-FBE demonstrates significant advantages in terms of parameter setting, noise robustness, and fault feature extraction sufficiency. The fault diagnosis model trained with the features extracted by PPSE-FBE can achieve higher recognition accuracy.