Planet bearing kinematics involve spinning-revolution coupling, resulting in complex and weak fault vibration signals that pose a tremendous challenge to fault diagnosis. Under time-varying speed conditions, the frequency characteristics of gear mesh vibrations overlap with those of planet bearing faults, severely interfering with their fault diagnosis. To address this issue, this paper proposes an order-frequency spectral correlation analysis method for non-stationary signals. The method removes the time-varying low-frequency amplitude envelope of the vibration signal and performs angular domain resampling to stabilize the order characteristics of gear components. Discrete random separation in the order domain is applied to eliminate gear vibration while retaining residual random components. These random components are inverse angular domain resampled to restore the original amplitude envelope, and planet bearing fault features are extracted through their order-frequency spectral correlation or coherence. This method enhances planet bearing fault features and improves the diagnosis capability under time-varying speed conditions. The principle of the method is demonstrated through numerical simulation analysis. Its performance is validated experimentally by successfully diagnosing localized fault on the inner and outer race and rolling elements of planet bearings.