基于GO法和RCM的惯性导航系统预测维修平台设计

针对惯性导航系统（INS）结构复杂、难以进行精确的系统可靠性分析和维修优化等问题，提出了一种基于GO法和以可靠性为中心的维修（RCM）的INS预测维修平台设计方法。利用GO法建立INS的可靠性分析模型—GO图，依据部件的寿命分布函数及时间应力采样更新部件的可靠度，实现部件残余寿命及系统可靠度的动态预测和评估。对不满足系统可靠性指标的INS，利用综合评价法权衡部件的贡献度、失效频度、检测度等影响因素，计算出各部件的维修优先度。最后按INS部件失效率恒定、失效率可变及故障部件隔离等不同情况分别进行仿真验证，结果证明所设计的INS预测维修平台是可行的、有效的，评价结果可为维修计划的制定提供参考依据。针对惯性导航系统（INS）结构复杂、难以进行精确的系统可靠性分析和维修优化等问题，提出了一种基于GO法和以可靠性为中心的维修（RCM）的INS预测维修平台设计方法。利用GO法建立INS的可靠性分析模型—GO图，依据部件的寿命分布函数及时间应力采样更新部件的可靠度，实现部件残余寿命及系统可靠度的动态预测和评估。对不满足系统可靠性指标的INS，利用综合评价法权衡部件的贡献度、失效频度、检测度等影响因素，计算出各部件的维修优先度。最后按INS部件失效率恒定、失效率可变及故障部件隔离等不同情况分别进行仿真验证，结果证明所设计的INS预测维修平台是可行的、有效的，评价结果可为维修计划的制定提供参考依据。

Design of Predictive Maintenance Platform for INS Based on GO Methodology and RCM

A design method of predictive maintenance platform based on GO methodology and reliability-centered maintenance(RCM) is proposed for the complex structure, reliability analysis and optimal maintenance of inertial navigation system (INS). GO methodology is applied to build the INS reliability analyses model—GO chart. In order to realize the dynamic prediction and evaluation of the residual lives of components and the system reliability, the components reliability are updated continuously based on life distribution functions and time stress samples. For the INS that fails to meet the system reliability index, the maintenance priorities of all components are given quantitatively by adopting the comprehensive evaluation method to balance the affecting factors of components: contribution to system, failure likelihood and detection difficulty. Finally, some simulations are processed under the situations of constant failure rate, variable failure rate and fault isolation of INS components. The results show that the proposed predictive maintenance platform for INS is feasible and effective, and the predicted results can be used as a reference for making scientific maintenance decisions.