Please use this identifier to cite or link to this item: http://elar.urfu.ru/handle/10995/118203
Title: Estimation of gas turbine technical condition using machine learning methods
Authors: Blinov, V. L.
Deryabin, G. A.
Issue Date: 2022
Publisher: Institute of Physics
Citation: Blinov V. L. Estimation of gas turbine technical condition using machine learning methods / V. L. Blinov, G. A. Deryabin // IOP Conference Series: Earth and Environmental Science. — 2022. — Vol. 1045. — Iss. 1. — 12170.
Abstract: This paper considers the method to estimate the technical condition of gas turbine power for natural gas transportation, using machine learning methods. Source data was used to archive gas-dynamic parameters from the automatic control system of the gas turbine. The method is based on changing the enthalpy of the natural gas before and after the centrifugal gas compressor is used for creating a dataset with measured parameters and power from the gas turbine. The actual power is determined from the line of modes for a certain period. The software is implemented using Python and the Scikit-learn library is used to create machine learning models. A mean average percentile error is chosen as the model quality criterion. In this paper, different sets of feature parameters and sample sizes are researched by the quality of the prediction machine learning models. Recommendations on the use of models are given. It has been established that the approach is not applicable for predicting future technical condition without the presence of data on a similar technical condition in the training sample. It is recommended to use the described approach to determine the technical condition in a period of operation in the past. © Published under licence by IOP Publishing Ltd.
URI: http://elar.urfu.ru/handle/10995/118203
Access: info:eu-repo/semantics/openAccess
Conference name: 2nd International Scientific and Practical Conference on Ensuring Sustainable Development in Thecontext of Agriculture, Green Energy, Ecology and Earth Science, ESDCA 2022
Conference date: 23 January 2022 through 27 January 2022
SCOPUS ID: 85134154786
ISSN: 17551307
DOI: 10.1088/1755-1315/1045/1/012170
metadata.dc.description.sponsorship: Terentyev S.Romanova I.Gibadullin A.Gibadullin A.
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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