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Creep Rupture Forecasting: A Machine Learning Approach to Useful Life Estimation

Creep Rupture Forecasting: A Machine Learning Approach to Useful Life Estimation

Stylianos Chatzidakis, Miltiadis Alamaniotis, Lefteri H. Tsoukalas
Copyright: © 2014 |Volume: 2 |Issue: 2 |Pages: 25
ISSN: 2166-7241|EISSN: 2166-725X|EISBN13: 9781466655744|DOI: 10.4018/ijmstr.2014040101
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MLA

Chatzidakis, Stylianos, et al. "Creep Rupture Forecasting: A Machine Learning Approach to Useful Life Estimation." IJMSTR vol.2, no.2 2014: pp.1-25. http://doi.org/10.4018/ijmstr.2014040101

APA

Chatzidakis, S., Alamaniotis, M., & Tsoukalas, L. H. (2014). Creep Rupture Forecasting: A Machine Learning Approach to Useful Life Estimation. International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), 2(2), 1-25. http://doi.org/10.4018/ijmstr.2014040101

Chicago

Chatzidakis, Stylianos, Miltiadis Alamaniotis, and Lefteri H. Tsoukalas. "Creep Rupture Forecasting: A Machine Learning Approach to Useful Life Estimation," International Journal of Monitoring and Surveillance Technologies Research (IJMSTR) 2, no.2: 1-25. http://doi.org/10.4018/ijmstr.2014040101

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Abstract

Creep rupture is becoming increasingly one of the most important problems affecting behavior and performance of power production systems operating in high temperature environments and potentially under irradiation as is the case of nuclear reactors. Creep rupture forecasting and estimation of the useful life is required to avoid unanticipated component failure and cost ineffective operation. Despite the rigorous investigations of creep mechanisms and their effect on component lifetime, experimental data are sparse rendering the time to rupture prediction a rather difficult problem. An approach for performing creep rupture forecasting that exploits the unique characteristics of machine learning algorithms is proposed herein. The approach seeks to introduce a mechanism that will synergistically exploit recent findings in creep rupture with the state-of-the-art computational paradigm of machine learning. In this study, three machine learning algorithms, namely General Regression Neural Networks, Artificial Neural Networks and Gaussian Processes, were employed to capture the underlying trends and provide creep rupture forecasting. The current implementation is demonstrated and evaluated on actual experimental creep rupture data. Results show that the Gaussian process model based on the Matérn kernel achieved the best overall prediction performance (56.38%). Significant dependencies exist on the number of training data, neural network size, kernel selection and whether interpolation or extrapolation is performed.

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