Wavelet-Enhanced Time-Frequency Analysis Method for Bearing Fault Detection of Rotating Machinery

In industrial settings, analyzing vibration signals from rolling bearings is a key method for diagnosing faults in rotating machinery. However, these faults often appear as a series of transient events with quickly changing frequencies, which makes it difficult for traditional time-frequency analysis techniques to accurately capture these characteristics. Methods like synchrosqueezing transmute and multi-synchrosqueezing transmute struggle with this task. In this study, we introduce a new method called the Wavelet-Enhanced Transient-Extracting Transform (WTET). Unlike traditional approaches that rely on instantaneous frequency in a time-domain model, WTET uses a group delay (GD) estimator in a frequency-domain model. This allows for more accurate characterization of bearing and rub impact fault signals. Additionally, WTET is better at resisting noise compared to the traditional wavelet transform because it isolates only the central coefficients in the time-frequency domain. WTET also retains the ability to reconstruct the original signal, which makes it effective at extracting different signal modes. Our analysis shows that WTET offers improved diagnostic capability for detecting bearing faults.