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基于离散危险模型的统计失效时间模型及其在汽轮机转子和轴用1Cr1Mo - 0.25V钢上的应用综述

A Review of Statistical Failure Time Models with Application of a Discrete Hazard Based Model to 1Cr1Mo-0.25V Steel for Turbine Rotors and Shafts.

作者信息

Evans Mark

机构信息

College of Engineering, Swansea University, Fabian Way, Crymlyn Burrows, Wales SA1 8EN, UK.

出版信息

Materials (Basel). 2017 Oct 17;10(10):1190. doi: 10.3390/ma10101190.

Abstract

Producing predictions of the probabilistic risks of operating materials for given lengths of time at stated operating conditions requires the assimilation of existing deterministic creep life prediction models (that only predict the average failure time) with statistical models that capture the random component of creep. To date, these approaches have rarely been combined to achieve this objective. The first half of this paper therefore provides a summary review of some statistical models to help bridge the gap between these two approaches. The second half of the paper illustrates one possible assimilation using 1Cr1Mo-0.25V steel. The Wilshire equation for creep life prediction is integrated into a discrete hazard based statistical model-the former being chosen because of its novelty and proven capability in accurately predicting average failure times and the latter being chosen because of its flexibility in modelling the failure time distribution. Using this model it was found that, for example, if this material had been in operation for around 15 years at 823 K and 130 MPa, the chances of failure in the next year is around 35%. However, if this material had been in operation for around 25 years, the chance of failure in the next year rises dramatically to around 80%.

摘要

要预测给定运行条件下运行材料在特定时长内的概率风险,需要将现有的确定性蠕变寿命预测模型(仅预测平均失效时间)与捕捉蠕变随机成分的统计模型相结合。迄今为止,这些方法很少被结合起来以实现这一目标。因此,本文前半部分对一些统计模型进行了综述,以帮助弥合这两种方法之间的差距。本文后半部分展示了使用1Cr1Mo - 0.25V钢进行一种可能的结合方式。用于蠕变寿命预测的威尔希尔方程被整合到一个基于离散风险的统计模型中——选择前者是因为其新颖性以及在准确预测平均失效时间方面已被证实的能力,选择后者是因为其在对失效时间分布进行建模时具有灵活性。使用该模型发现,例如,如果这种材料在823K和130MPa下运行了约15年,那么下一年失效的概率约为35%。然而,如果这种材料已经运行了约25年,下一年失效的概率会急剧上升至约80%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0836/5666996/05dca48dcdcb/materials-10-01190-g001.jpg

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