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Failures prediction based on performance monitoring of a gas turbine: a binary classification approach

Publication date: 16.02.2018

Schedae Informaticae, 2017, Volume 26, pp. 9 - 21

https://doi.org/10.4467/20838476SI.17.002.7246

Authors

,
Bartłomiej Mulewicz
Reliability Solutions, ul. Lublańska 34, 31-476 Kraków, Poland
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,
Mateusz Marzec
Reliability Solutions, ul. Lublańska 34, 31-476 Kraków, Poland; AGH University of Science and Technology, Faculty of Applied Mathematics, al. Mickiewicza 30, 30-059 Kraków, Poland
All publications →
,
Paweł Morkisz
Reliability Solutions, ul. Lublańska 34, 31-476 Kraków, Poland; AGH University of Science and Technology, Faculty of Applied Mathematics, al. Mickiewicza 30, 30-059 Kraków, Poland
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Piotr Oprocha
Reliability Solutions, ul. Lublańska 34, 31-476 Kraków, Poland; AGH University of Science and Technology, Faculty of Applied Mathematics, al. Mickiewicza 30, 30-059 Kraków, Poland
All publications →

Titles

Failures prediction based on performance monitoring of a gas turbine: a binary classification approach

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.

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Information

Information: Schedae Informaticae, 2017, Volume 26, pp. 9 - 21

Article type: Original article

Titles:

Polish:

Failures prediction based on performance monitoring of a gas turbine: a binary classification approach

English:

Failures prediction based on performance monitoring of a gas turbine: a binary classification approach

Authors

Reliability Solutions, ul. Lublańska 34, 31-476 Kraków, Poland

Reliability Solutions, ul. Lublańska 34, 31-476 Kraków, Poland; AGH University of Science and Technology, Faculty of Applied Mathematics, al. Mickiewicza 30, 30-059 Kraków, Poland

Reliability Solutions, ul. Lublańska 34, 31-476 Kraków, Poland; AGH University of Science and Technology, Faculty of Applied Mathematics, al. Mickiewicza 30, 30-059 Kraków, Poland

Reliability Solutions, ul. Lublańska 34, 31-476 Kraków, Poland; AGH University of Science and Technology, Faculty of Applied Mathematics, al. Mickiewicza 30, 30-059 Kraków, Poland

Published at: 16.02.2018

Article status: Open

Licence: CC BY-NC-ND  licence icon

Percentage share of authors:

Bartłomiej Mulewicz (Author) - 25%
Mateusz Marzec (Author) - 25%
Paweł Morkisz (Author) - 25%
Piotr Oprocha (Author) - 25%

Article corrections:

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Publication languages:

English