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激光自适应光学系统可靠性改进效果评估OA北大核心CSTPCD

Evaluation of reliability improvement effect on laser adaptive optics systems

中文摘要英文摘要

随着自适应光学技术在激光领域的发展,工程上以经典自适应光学(AO)系统为基础,增加了多种基于软件监测和硬件保护的改进措施以保证激光AO系统稳定连续出光.面对结构复杂度提升带来的可靠性挑战,如何构建系统失效模型对激光AO系统可靠性进行评估,成为影响激光AO系统发展的重要一环.本文以激光光稳净化AO系统为例,提出使用动态故障树方法对激光AO系统可靠性进行评估,根据设备间动态关系建立动态故障树(DFT),结合厂家信息、疲劳寿命试验与历史数据估计得到底事件失效率,使用二元决策图和马尔可夫模型求解得到DFT的可靠性参数.使用DFT分析增加改进措施的AO系统可靠运行时间,结果相对于基本故障树获得了十倍以上的提高.实际系统调试期间,在预计的可靠运行时间内未发生自因故障,与DFT估计结果一致.验证了应用DFT方法评估增加改进措施后的激光AO系统可靠性更准确.

With the development of adaptive optics(AO)technology in laser field,a variety of improvement measures based on software monitoring and hardware protection have been added to the classical AO system to ensure stable and continuous light output of laser AO system.Facing the reliability challenge brought by the increase of structural complexity,how to build a system failure model to evaluate the reliability of laser AO system has become an important part of the development of laser AO system.In this paper,a dynamic fault tree(DFT)method is proposed to evaluate the reliability of laser AO system,and the dynamic fault tree is established according to the dynamic relationship between the equipment.The bottom event failure rate is estimated by combining the manufacturer information,fatigue life test and historical data.The reliability parameters of DFT are obtained by using binary decision graph and Markov model.Using DFT to analyse the reliable running time of the AO system with the improvement measures,the result shows more than ten times improvement relative to the basic fault tree.In the actual system joint commissioning,no self-induced failure occurred during the expected reliable running time,which is consistent with the DFT estimate.It is proved that the reliability evaluation of laser AO system with improvement measures is more accurate by using DFT method.

贾启旺;李新阳;罗曦;甘永东;马瑞浩;梅月;斯那卓玛

中国科学院光场调控科学技术全国重点实验室,成都 610209||中国科学院自适应光学重点实验室,成都 610209||中国科学院光电技术研究所,成都 610209

电子信息工程

自适应光学动态故障树可靠性评估马尔可夫模型

adaptive opticsdynamic fault treereliability evaluationMarkov model

《强激光与粒子束》 2024 (006)

24-33 / 10

中国科学院创新基金项目(CXJJ-16S022)

10.11884/HPLPB202436.230436

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