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

• Autorzy: Mulewicz B. , Marzec M. , Morkisz P. , Oprocha P.

Identyfikatory

DOI

10.4467/20838476SI.17.002.7246

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

Predictive Maintenance; deep learning; binary classification; convolutional neural network

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2017
• Tom: Vol. 26
• Strony: 9--21
• Opis fizyczny: Bibliogr. 22 poz., tab.

Twórcy

autor

Mulewicz B.

• Reliability Solutions, ul. Lubla«ska 34, 31-476 Kraków, Poland

autor

Marzec M.

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

autor

Morkisz P.

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

autor

Oprocha P.

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

Bibliografia

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• [17] Fukushima, K., Miyake S., Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. Competition and cooperation in neural nets, 1982, pp. 267-285.
• [18] Ioffe S., Szegedy C., Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning, 2015, pp. 448-456.
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• [20] Zeiler M.D., Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701, 2012.
• [21] He K., Zhang X., Ren S., Sun J., Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision, 2015, pp. 1026-1034.
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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).