自动驾驶仪PHM系统健康评估方法研究

Research on health assessment method of autopilot prognostics and health management system

PHM(prognostics and health management)系统健康评估作为连接故障诊断和故障预测的纽带,具有十分重要的作用。针对自动驾驶仪PHM系统健康评估存在的多工况、非线性和小子样问题,提出了一种面向自动驾驶仪PHM系统的贝叶斯网络简化推理模型,建立了一种基于可变信息的改进贝叶斯节点和迭代交叉熵测度的变模型快速推理算法。算法理论仿真实验验证了其有效性和工程可行性,最后在某型直升机电动舵机平台上验证了该改进算法,具有较好的工程应用前景。

As the linkage between fault diagnosis and prognostics, the health assessment of airborne prognostics and health management （PHM） system plays an essential role. Aiming at the problems of multi-mode, non-linear and small sample in current autopilot PHM health assessment, a Bayesian network inference model based on variable information is built. An improved Bayesian-ball and iterative cross-entropy inference algorithm is proposed. Theoretical simulations prove the effectiveness and engineering feasibility of the proposed algorithm. The improved algorithm was validated on the electronic steering gear of a certain helicopter, which indicates that the algorithm has good application prospect.