针对传统的Markov模型可靠性评价方法难以体现复杂可变工作条件对电子系统可靠性影响的问题,根据云模型融合随机性和模糊性的特点,将云模型和Markov模型相结合,提出了基于Markov模型的云可靠性评价方法.首先根据电子产品和维修人员的特点,建立失效率和修复率关于环境条件和维修人员生理状况的适应能力云模型,然后根据实际的工作条件情况建立工作条件云模型,最后由适应能力云模型和工作条件云模型通过X条件云发生器生成失效率云和修复率云,并作为输入参数代入Markov模型进行可靠性评价.该方法考虑了环境条件和维修人员生理状况对系统可靠性的影响,其评价结果更接近于系统的真实情况.采用该方法对某双机冷备系统的分析表明:在温暖、电压稳定和维修人员身体状况良好时,工作条件变化导致系统可靠度和可用度的下降幅度低于0.5％；在寒冷、电压不稳和维修人员身体状况一般时,工作条件变化导致系统可靠度和可用度的下降幅度最大可达90％,与实际情况相符.%Since the traditional reliability evaluation method based on Markov model can't reflect the effects of complex and variable working conditions on the reliability of electronic system, a reliability evaluation method based on Markov model and cloud theory is proposed by combining Markov model with cloud model. Suitability cloud models of failure rate and repair rate are built according to the characteristics of environment and maintainers. Then working condition cloud models are made in terms of the real working conditions. The failure rate cloud and the repair rate cloud are generated from the suitability cloud models and the working condition cloud models through X condition cloud generator. Finally the failure rate cloud and repair rate cloud are used as input parameters of Markov model to evaluate the reliability. Since the effects of environmental conditions and physiological condition are taken into account in the proposed method, the reliability evaluation is more accurate than that of the method based on Markov model. Analysis of hot-standby system using the proposed method shows that the variation of conditions will cause 5% or fewer decline under good work conditions and up to 90% decline under bad work conditions, which is in accordance with the system's actual situation.
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