@article{b4990972-cf64-4f0d-baa3-6de5b4ad9fbb,
author = {Bartłomiej Mulewicz, Mateusz Marzec, Paweł Morkisz, Piotr Oprocha},
title = {Failures prediction based on performance monitoring of a gas turbine: a binary classification approach},
journal = {Schedae Informaticae},
volume = {2017},
number = {Volume 26},
year = {2018},
issn = {1732-3916},
pages = {9-21},keywords = {Predictive Maintenance; Deep Learning; binary classification; convolutional neural network},
abstract = {This paper is dedicated to employ novel technique of deep learning for machines failures prediction. General idea of how to transform sensor data into suitable data set for prediction is presented. Then, neural network architecture that is very successful in solving such problems is derived. Finally, we present a case study for real industrial data of a gas turbine, including results of the experiments.},
doi = {10.4467/20838476SI.17.002.7246},
url = {https://ejournals.eu/en/journal/schedae-informaticae/article/failures-prediction-based-on-performance-monitoring-of-a-gas-turbine-a-binary-classification-approach}
}