The insulation of the motor directly affects the safe operation of the motor. In order to enhance the accuracy of predicting the remaining useful life (RUL) of the motor at time-varying temperature, this study takes into account individual differences establishes a RUL prediction model. Temperature is identified as the primary factor affecting insulation life, and a degradation model based on Wiener degradation process and Arrhenius formula is developed. To solve unknown operating temperatures in subsequent motors, an equivalent temperature is utilized instead of actual temperature changes, leading to a RUL prediction model. Considering individual differences, model parameters are treated as random variables and estimated using maximum likelihood method to obtain prior distribution of these parameters. Bayesian inference and Markov Chain Monte Carlo (MCMC) method are employed for updating model parameters and obtaining posterior distribution. Simulation examples comparing results before and after parameter updates demonstrate that updating model parameters can enhance accuracy.