The oil spectral data are introduced to indicate the performance degradation and the remaining useful life(RUL) prediction in the reliability evaluation of Power shift steering transmission(PSST). Because of the PSST’s stochastic degradation and spectral measurement error, the measured data inevitably contain the stochasticity of the degradation and the uncertainty of spectrum measurement. However, in current studies of RUL prediction based on oil spectral data, no one has been reported to consider the effect of degradation stochasticity and measurement uncertainty on the prediction of RUL. Thus, aimed at reducing the adverse impact of measurement error of oil spectrum data on the remaining useful life(RUL) prediction of Power shift steering transmission(PSST), a degradation modeling method considering degradation stochasticity and measurement uncertainty is proposed. The concept of RUL of PSST is defined based on the concept of first hit time(FHT) of stochastic process. The parameters of the degradation model are estimated using the maximum likelihood method. The degradation state of the PSST is estimated and updated in real time using Kalman filtering technique, and the RUL distributions considering the system degradation stochasticity and spectral data measurement uncertainty are obtained. The experimental results show that the degradation modeling method proposed in this paper can accurately estimate the running state of the device and avoid the limitation of using the condition maintenance time to maintain the equipment. The time interval of condition-based maintenance has extended as 193 Mh (113.5%), and the RUL prediction method considering uncertain measurements is superior to the method without considering.