Research on status-driven hybrid evaluation method for mission success probability

Mission Success Probability is a top-level indicator that integrates reliability, maintainability, and spare parts security during the system's mission. Numerical simulation is used for mission success evaluation of complex system because it can consider any form of system structure and component failure distribution model. In actual engineering, system components usually deteriorate or fail with the extension of service time and need to be repaired or replaced, so there are dynamic changes in component status during the life cycle. To evaluate the mission success in such dynamic scenarios, it is necessary to repeat the simulation based on the whole system once the local parameters are adjusted. Despite the continuous improvement in computational performance, traditional numerical simulations are not well matched for rapid evaluation and timely decision making in dynamic scenarios. Therefore, an evaluation method that mixes proxy model and simulation sampling is proposed in this paper. The method uses the simulation to complete the data acquisition, and then analyzes the data by inverse function sampling and Copula function to obtain the model training samples, and finally realizes the approximate description of the local unit simulation process by proxy model. The experimental results show that the method can reduce the repetitive simulation scale from the whole system to local unit while ensuring high accuracy, avoiding many repeated calculations in variable scenarios.

Automated summary

Mission Success Probability is a top-level indicator that considers reliability, maintainability, and spare parts security during the system's mission. Numerical simulation is used for evaluating mission success in complex systems with varying component statuses. However, traditional simulations are not suitable for dynamic scenarios requiring rapid evaluation and decision making. Therefore, this paper proposes a method that combines proxy models and simulation sampling to achieve efficient and accurate evaluations. Experimental results show that this method reduces the need for repetitive simulations while maintaining high accuracy.