As an emerging simulation technology in the field of system modeling & simulation, equipment parallel simulation has become research emphasis. In the field of equipment maintenance support, the outstanding problem of equipment remaining useful life(RUL) prediction is analyzed, i.e., the stable model parameters without self-evolution ability, which has become the primary factor that hinders adaptive prediction of equipment remaining useful life. Combined with parallel systems theory, equipment remaining useful life prediction oriented parallel simulation framework is proposed on the basis of modeling analysis and Wiener state space model is taken as the basic simulation model in the framework. Driven by the dynamic implanted equipment degradation observation data, the model parameters are updated online by using expectation maximum(EM) algorithm and the data assimilation (DA) between simulation outputs and observation data is executed by using Kalman filter(KF), so as to realize dynamic evolution of the simulation model. The simulation model evolution which makes the simulation outputs close to equipment real degradation state provides high fidelity model and data for equipment remaining useful life prediction accurately. The framework is verified by the performance degradation data of a bearing. The simulation results show that the parallel simulation method can accurately simulate the equipment performance degradation process and the adaptive prediction of equipment remaining useful life is realized on the basis of the improved prediction accuracy, proving the feasibility and effectiveness of parallel simulation method.