Dual-Level Polynomial Resampling Extracting Transform and Its Application to Bearing Fault Diagnosis at Variable Speeds

Abstract

Bearing fault diagnosis in construction machinery presents significant challenges due to the variable rotating speeds of mechanical equipment and the influence of industrial noise. The key aspect of this diagnosis lies in accurately extracting and identifying the instantaneous fault characteristic frequency (IFCF) from the background noise of the vibration signal. To address this issue, we propose a novel time–frequency (TF) analysis (TFA) method called dual-level polynomial resampling extracting transform (D-PRET). The D-PRET method offers a high-quality TF representation (TFR), combining energy concentration, precise IFCF estimation, and effective elimination of noise interference. This approach ensures reliable extraction and identification of bearing IFCFs, leading to a more dependable fault diagnosis result. Numerical simulations and two experimental cases demonstrate the effectiveness of D-PRET in bearing fault diagnosis applications.