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Predicting length of fatigue cracks by means of machine learning algorithms in the small-data regime
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Institute of Fundamental Technological Research, Polish Academy of Sciences, ul. Pawińskiego 5b, 02-106 Warsaw, Poland
Baker Hughes, BH Poland sp. z o.o., Aleja Krakowska 110/114, 02-256 Warsaw, Poland
Baker Hughes, Via Felice Matteucci 2, 50127 Florence, Italy
Publication date: 2021-09-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(3):575-585
  • Machine learning algorithms can be utilized effectively in the small-data regime.
  • The length of fatigue cracks can be predicted based on operational data of the engine.
  • The lowest root mean squared error (RMSE) is achieved with the AdaBoost.R2 algorithm.
  • Small datasets should be processed in a fully controlled manner to get valuable results.
  • A custom cost function may favor certain solutions and drives capabilities of the model.
In this paper several statistical learning algorithms are used to predict the maximal length of fatigue cracks based on a sample composed of 31 observations. The small-data regime is still a problem for many professionals, especially in the areas where failures occur rarely. The analyzed object is a high-pressure Nozzle of a heavy-duty gas turbine. Operating parameters of the engines are used for the regression analysis. The following algorithms are used in this work: multiple linear and polynomial regression, random forest, kernel-based methods, AdaBoost and extreme gradient boosting and artificial neural networks. A substantial part of the paper provides advice on the effective selection of features. The paper explains how to process the dataset in order to reduce uncertainty; thus, simplifying the analysis of the results. The proposed loss and cost functions are custom and promote solutions accurately predicting the longest cracks. The obtained results confirm that some of the algorithms can accurately predict maximal lengths of the fatigue cracks, even if the sample is small.
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