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Semi-Markov models with an application to power-plant reliability analysis

机译:半马尔可夫模型及其在电厂可靠性分析中的应用

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摘要

Systems with, (1) a finite number of states, and (2) random holding times in each state, are often modeled using semi-Markov processes. For general holding-time distributions, closed formulas for transition probabilities and average availability are usually not available. Recursion procedures are derived to approximate these quantities for arbitrarily distributed holding-times; these recursion procedures are then used to fit the semi-Markov model with Weibull distributed holding-times to actual power-plant operating data. The results are compared to the more familiar Markov models; the semi-Markov model using Weibull holding-times fits the data remarkably well. In particular comparing the transition probabilities shows that the probability of the system being in the state of refitting converges more quickly to its limiting value as compared to convergence in the Markov model. This could be because the distribution of the holding-times in this state is rather unlike the exponential distribution. The more flexible semi-Markov model with Weibull holding-times describes more accurately the operating characteristics of power-plants, and produces a better fit to the actual operating data.
机译:通常使用半马尔可夫过程对具有(1)有限数量的状态以及(2)每个状态中的随机保持时间的系统进行建模。对于一般的保持时间分布,通常没有转换概率和平均可用性的封闭公式。推导了递归程序,以针对任意分布的保持时间近似估算这些数量。然后使用这些递归程序将具有Weibull分布保持时间的半马尔可夫模型拟合到实际电厂运行数据。将结果与更熟悉的马尔可夫模型进行比较;使用Weibull保持时间的半马尔可夫模型非常适合该数据。特别地,比较转移概率表明,与马尔可夫模型中的收敛性相比,系统处于重新装配状态的概率更快地收敛至其极限值。这可能是因为在这种状态下,保持时间的分布与指数分布完全不同。具有Weibull保持时间的更灵活的半马尔可夫模型可以更准确地描述电厂的运行特性,并且可以更好地拟合实际运行数据。

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