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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 / Mark Evans

Materials, Volume: 10, Issue: 10, Start page: 1190

Swansea University Author: Mark Evans

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DOI (Published version): 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 rand...

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Published in: Materials
ISSN: 1996-1944
Published: MDPI AG 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa36106
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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%.
Keywords: creep; Wilshire equation; deterministic and random components; parametric and non-parametric statistical models; discrete hazard based models
College: College of Engineering
Issue: 10
Start Page: 1190