修复如新下基于RP和MC的故障注入样本生成

Fault injection sample generation based on renewal process and Monte Carlo in perfect maintenance

由于测试性虚拟试验可有效弥补测试性实物试验的不足,但同时也对故障注入样本生成提出新的要求。在可修系统修复如新假设下,提出一种基于更新过程理论和蒙特卡罗仿真的故障注入样本模拟生成方法。首先分析指出修复如新假设下可修系统的故障发生过程是随机过程,并用更新过程描述修复如新下可修系统的故障发生过程;然后,基于统计仿真实验中的蒙特卡罗方法,对事后维修模式下的故障发生过程进行抽样,实现故障注入样本的模拟生成;最后,以某稳定跟踪平台上的陀螺和运动控制器为案例进行实验。由于考虑了多种寿命分布类型,模拟生成的故障样本在样本量和样本结构上符合实际情况,所提出的方法能有效指导修复如新和事后维修情况下测试性虚拟验证试验中的故障注入。

Virtual testability test can effectively compensate for the shortcomings of physical testability test. However, the fault injection sample generation method needs to be changed and improved. Under the assumption of perfect maintenance for repairable system, a novel approach for fault injection sample generation based on renewal process theory and Monte Carlo （MC） simulation is proposed. Firstly, it is pointed out that the fault occurrence process of repairable system is a stochastic process, and the process is mathematically described with renewal process theory. Secondly, the fault injection sample generation based on Monte Carlo simulation is proposed, and the fault occurrence processes in breakdown maintenance are sampled with simulation. Experiments on the gyroscope and motion controller in a stable tracking platform were carried out. The results show that because a variety of life distribution types are considered, the size and structure of the generated fault injection sample are reasonable. The proposed method can effectively guide the fault injection in virtual testability demonstration test in perfect and breakdown maintenance.