Optimal phase synchronous averaging: A novel approach for vibration feature extraction of rotating structures

• Enhancements and innovations in traiditional Time Synchronous Averaging algorithm. • A Novel Visualization Approach for Rotating Structural Signals with Improved Interpretability. • Investigation into Bearing Damage Mechanisms via Aligned Vibration Signal Analysis. • Potential for Extracting New Information from Traditional Datasets. Phase shifts in the fault signals of rotating structures pose significant challenges to data analysis and fault diagnosis. This paper proposes a novel phase synchronization model that leverages waveform matching and multi-sensor data fusion for vibration data collected from bearings to address this issue. We have developed an optimization model to maximize the signal-to-noise energy ratio through cyclic sampling of each data point. To solve this optimization problem, we introduce Optimal Phase Synchronous Averaging (Optimal-PSA), a method that integrates super-resolution sampling with the Time Synchronous Averaging (TSA) algorithm. Our approach has been rigorously tested using the publicly available Case Western Reserve University (CWRU) dataset. To quantify signal phase shift, we introduce the Phase Consistency Coefficient (PCC) and Rotary Feature Graph (RF-Graph) as novel metrics. PCC quantifies alignment efficacy via the ratio of aligned signal standard deviation to Optimal-PSA amplitude; RF-Graph visualizes signal amplitude and standard deviation at actual spatial locations by mapping periodic signals onto a bearing-referenced circular circumference. The results demonstrate that our algorithm significantly enhances the identification of bearing fault characteristics, achieving an average improvement factor of 1.98 in the PCC metric relative to the traditional TSA algorithm across various operating conditions. This method paves the way for bearing fault analysis in future industrial applications.