Abstract: To address the challenges of weak signal extraction and transient disturbance susceptibility in online monitoring of XLPE cable insulation aging, this study proposes an aging signal identification method based on time-frequency panoramic information, enabling precise non-stop power-off diagnosis of cable insulation status. First, a nonlinear equivalent model of XLPE cable insulation aging regions was established to reveal the fundamental differences in time-frequency distribution between aging signals and transient disturbances (e.g., capacitor switching) from a mechanistic perspective: aging signals exhibit persistent high-frequency components, while disturbance signals show exponential decay of high-frequency components. Subsequently, the Robust Masked Time-Frequency Decomposition (RMCMD) method was applied to decompose collected signals, constructing a time-frequency spectral matrix. For the first time, Time-Frequency Energy Spectrum Entropy (TFEE) was introduced as a quantified feature to characterize the complexity of signal energy distribution. By setting appropriate thresholds, automatic differentiation between aging signals and transient disturbances was achieved. Simulation results demonstrate that the proposed method accurately identifies insulation aging signals under varying noise levels, aging severity, and location conditions, with significantly higher recognition accuracy for transient disturbance signals compared to traditional time-frequency analysis methods. The online diagnostic approach based on RMCMD algorithm and time-frequency spectral entropy proposed in this study can effectively and robustly identify early-stage insulation aging in XLPE cables, providing a non-stop, easy-to-implement technical pathway for condition-based maintenance.