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Combustion anomalies detection for a thermal furnace based on Recurrent Neural Networks. / Abdurakipov, Sergey; Butakov, Evgenii.

In: Journal of Physics: Conference Series, Vol. 1105, No. 1, 012043, 28.11.2018.

Research output: Contribution to journalConference articlepeer-review

Harvard

Abdurakipov, S & Butakov, E 2018, 'Combustion anomalies detection for a thermal furnace based on Recurrent Neural Networks', Journal of Physics: Conference Series, vol. 1105, no. 1, 012043. https://doi.org/10.1088/1742-6596/1105/1/012043

APA

Abdurakipov, S., & Butakov, E. (2018). Combustion anomalies detection for a thermal furnace based on Recurrent Neural Networks. Journal of Physics: Conference Series, 1105(1), [012043]. https://doi.org/10.1088/1742-6596/1105/1/012043

Vancouver

Abdurakipov S, Butakov E. Combustion anomalies detection for a thermal furnace based on Recurrent Neural Networks. Journal of Physics: Conference Series. 2018 Nov 28;1105(1):012043. doi: 10.1088/1742-6596/1105/1/012043

Author

Abdurakipov, Sergey ; Butakov, Evgenii. / Combustion anomalies detection for a thermal furnace based on Recurrent Neural Networks. In: Journal of Physics: Conference Series. 2018 ; Vol. 1105, No. 1.

BibTeX

@article{a0f4b80d47f34cb1bf19c39e8677cc69,
title = "Combustion anomalies detection for a thermal furnace based on Recurrent Neural Networks",
abstract = "This paper describes the application of Recurrent Neural Networks (RNN) for effectively detecting anomalies in time series data obtained from experimental study of the combustion and gasification of mechanically activated coal fuel in a thermal furnace. We train Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) units to learn the normal time series patterns and predict anomaly values. The resulting prediction errors between real and expected values are analyzed to give anomaly scores. To investigate the most suitable configuration of RNN and evaluate the effectiveness of the anomaly detection model, we used three datasets of real-world data that contain several types of anomalies. The developed RNN algorithm detected 9 out the 9 collective anomalies in the hold-out sample with one false positive anomaly event.",
keywords = "ACTIVATION",
author = "Sergey Abdurakipov and Evgenii Butakov",
note = "Publisher Copyright: {\textcopyright} Published under licence by IOP Publishing Ltd.; 34th Siberian Thermophysical Seminar Dedicated to the 85th Anniversary of Academician A. K. Rebrov, STS 2018 ; Conference date: 27-08-2018 Through 30-08-2018",
year = "2018",
month = nov,
day = "28",
doi = "10.1088/1742-6596/1105/1/012043",
language = "English",
volume = "1105",
journal = "Journal of Physics: Conference Series",
issn = "1742-6588",
publisher = "IOP Publishing Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Combustion anomalies detection for a thermal furnace based on Recurrent Neural Networks

AU - Abdurakipov, Sergey

AU - Butakov, Evgenii

N1 - Publisher Copyright: © Published under licence by IOP Publishing Ltd.

PY - 2018/11/28

Y1 - 2018/11/28

N2 - This paper describes the application of Recurrent Neural Networks (RNN) for effectively detecting anomalies in time series data obtained from experimental study of the combustion and gasification of mechanically activated coal fuel in a thermal furnace. We train Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) units to learn the normal time series patterns and predict anomaly values. The resulting prediction errors between real and expected values are analyzed to give anomaly scores. To investigate the most suitable configuration of RNN and evaluate the effectiveness of the anomaly detection model, we used three datasets of real-world data that contain several types of anomalies. The developed RNN algorithm detected 9 out the 9 collective anomalies in the hold-out sample with one false positive anomaly event.

AB - This paper describes the application of Recurrent Neural Networks (RNN) for effectively detecting anomalies in time series data obtained from experimental study of the combustion and gasification of mechanically activated coal fuel in a thermal furnace. We train Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) units to learn the normal time series patterns and predict anomaly values. The resulting prediction errors between real and expected values are analyzed to give anomaly scores. To investigate the most suitable configuration of RNN and evaluate the effectiveness of the anomaly detection model, we used three datasets of real-world data that contain several types of anomalies. The developed RNN algorithm detected 9 out the 9 collective anomalies in the hold-out sample with one false positive anomaly event.

KW - ACTIVATION

UR - http://www.scopus.com/inward/record.url?scp=85058245061&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/1105/1/012043

DO - 10.1088/1742-6596/1105/1/012043

M3 - Conference article

AN - SCOPUS:85058245061

VL - 1105

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012043

T2 - 34th Siberian Thermophysical Seminar Dedicated to the 85th Anniversary of Academician A. K. Rebrov, STS 2018

Y2 - 27 August 2018 through 30 August 2018

ER -

ID: 17853207