针对关键设备启动可靠性问题的重要性，以及传统启动可靠性验证试验理论对启动试验的复杂性考虑不足的现实，本课题将在深入调研的基础上，首先，以启动试验复杂性的成因为切入点，依据启动可靠性验证试验传统模型理论和Consecutive-k系统可靠性理论，构建启动可靠性验证试验拓展模型；其次，针对拓展模型构建概率指标体系，并应用有限马尔可夫链嵌入法和蒙特卡罗模拟法进行指标求解研究；再次，以试验次数或成本最小化为目标函数，通过风险细分和多维OC曲面构建约束条件，进而建立参数优化模型，并进行模型求解研究；最后，针对启动可靠性验证试验数据的特点，进行基于MCMC的启动可靠性贝叶斯估计方法研究。.本课题研究成果可直接应用于各种复杂情况下的最佳启动可靠性验证试验方案设计，以及相关标准的制订。此外，本课题研究还将进一步丰富可靠性试验理论，推动系统可靠性、统计游程、有限马尔可夫链嵌入法等相关理论和研究方法的发展。

According to the importance of start-up reliability of key equipments, and the insufficiency of theoretical gap on complexity of traditional start-up demonstration tests in complex cases, further study is required. First, advanced models of start-up demonstration tests will be constructed based on the cause of various complex situations, theory of traditional models of start-up demonstration tests, and theory of Consecutive-k system reliability. Second, probability indexes will be constructed in view of the advanced models, and the relevant indicators will be solved by using the finite Markov chain embedding approach and Monte Carlo simulation. Then, by taking the minimization of the times or costs of tests as objective function, and using constraints of risk segmentation and multidimensional OC surface, the optimization model will be given and the relevant indicators will be solved. Finally, according to the characters of the data obtained from start-up demonstration tests, we will propose Bayesian Estimation of start-up reliability based on Markov Chain Monte Carol (MCMC) methods. .This research can be directly applied to all kinds of complicated start-up demonstration tests design and the constitution of relative standards. Moreover, this research can further expand the reliability test theory, and accelerate the development of system reliability, statistical runs, finite Markov chain embedding approach, and so on.