基于数字孪生的光电探测系统性能预测

To solve the problem of electro-optical system for its performance monitoring, a digital twin model based on Dynamic Bayesian Network (DBN) was proposed. In this model, a system-level performance indicator from the perspective of energy domain using Modulation Transfer Function (MTF) was developed, which avoided tedious modelling of performance interactions between the multiple subsystems of electro-optical system. DBN was constructed from the evolution of MTF to denote the dynamic performance degradation process and the propagation of epistemic uncertainty. To make the digital twin model capable of tracking and predicting the system performance states, Particle Filter (PF) was proposed as the inference algorithm for DBN. A real dataset collected in the laboratory environment was used to validate the feasibility of the digital twin model and verify the effectiveness of PF inference algorithm. The results showed that the proposed method was effective for joint estimation of states and parameters, and the prediction of electro-optical system on-line health-status was achieved.