Instytut Podstawowych Problemów Techniki
Polskiej Akademii Nauk

Pracownicy

mgr inż. Maciej Badora

Zakład Technologii Inteligentnych (ZTI)
doktorant
e-mail:

Ostatnie publikacje
1.  Badora M., Sepe M., Bielecki M., Graziano A., Szolc T., Predicting length of fatigue cracks by means of machine learningalgorithms in the small-data regime, EKSPLOATACJA I NIEZAWODNOŚĆ - MAINTENANCE AND RELIABILITY, ISSN: 1507-2711, DOI: 10.17531/ein.2021.3.19, Vol.23, No.3, pp.575-585, 2021

Streszczenie:
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.

Słowa kluczowe:
empirical models, fatigue cracks, predictive maintenance, regression analysis, small data, statistical learning, turbomachinery

Afiliacje autorów:
Badora M. - IPPT PAN
Sepe M. - inna afiliacja
Bielecki M. - inna afiliacja
Graziano A. - inna afiliacja
Szolc T. - IPPT PAN
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