Abstract <p>This study investigates the application of acoustic emission (AE) signals for monitoring fatigue damage during high-cycle fatigue (HCF) tests and proposes a fatigue damage monitoring strategy based on the Bhattacharyya coefficient (BC). A comprehensive description of the AE signal processing workflow is provided, encompassing the acquisition of signal waveform data, the calculation of probability distributions, and the utilization of the BC to quantify the similarity between reference and true distributions. Comparative analysis with traditional infrared thermography demonstrates that BC exhibits superior sensitivity in HCF tests, effectively capturing the progressive stages of fatigue damage. Additionally, the study evaluates the influence of varying bin widths on BC calculations and identifies an optimal bin width (BW). The optimal bin width not only balances the monitoring sensitivity and stability but also reduces the influence of external noise.</p>