Vibration signal analysis is crucial for gearbox fault diagnosis, yet its inherent stochastic nature can challenge the identification of fault-induced changes amidst signal variability. This paper introduces a novel fault diagnosis approach, the Fault-Affected Signal Energy Ratio (FASER), designed to account for the uncertainty in vibration signals under repetitive operating conditions. The uncertainty is quantified using probability distributions of coefficients derived from Short-Time Fourier Transform (STFT) applied to normal state signals, employing Kernel Density Estimation and Kullback-Leibler divergence. This combined approach enables the quantification of uncertainty in signals with non-constant uncertainty, dependent on time and frequency, when converted to STFT. These distributions facilitate the assessment of the FASER in new signals extracted under synchronized operating conditions using our proposed method. This newly proposed FASER, accompanied by adaptive threshold to address feature randomness from STFT indices’ correlation, serves as a robust health indicator. Experimental validation demonstrates the effectiveness of the proposed method in accurate gearbox fault diagnosis under repetitive operating conditions, along with the capability to estimate fault severity levels.