With the development of detection and sensing technology, status of systems such as wind turbine blades can be detected, and remaining useful life(RUL) can be predicted according to the detection results. However, such systems are greatly impacted by environmental shock in operation. It is worth studying how to predict the RUL of systems under impact and how to make economic and reliable maintenance decisions link the prediction results. In this paper, the RUL prediction and the predictive maintenance(PdM) decision are studied for the continuous deteriorating system with condition detectability. Firstly, the accelerated shock damage degradation model and the RUL prediction model are modeled considering the degradation and the degradation-related shock damage. Secondly, the combined maintenance policy of condition based maintenance(CBM) and PdM on periodic inspection is formulated, and the occurrence probability of different maintenance activities is derived. Then, the maintenance decision model is constructed with the objective of minimizing the long-term average cost rate and the decision variables of inspection period and failure rate threshold, and the optimized solution is given. Finally, the applicability and effectiveness of the model are verified by taking the fan turbine blade as an example, and the sensitivity analysis of the system parameters is carried out. The optimized solution is compared with the maintenance decision results without accelerated shock damage and without prediction.