Please use this identifier to cite or link to this item:
http://hdl.handle.net/123456789/14633
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kozlenko, Mykola | - |
dc.contributor.author | Kuz, Mykola | - |
dc.contributor.author | Zamikhovska, Olena | - |
dc.contributor.author | Zamikhovskyi, Leonid | - |
dc.contributor.author | Козленко, Микола Іванович | - |
dc.contributor.author | Кузь, Микола Васильович | - |
dc.contributor.author | Заміховська, Олена | - |
dc.contributor.author | Заміховський, Леонід Михайлович | - |
dc.date.accessioned | 2023-01-10T07:39:53Z | - |
dc.date.available | 2023-01-10T07:39:53Z | - |
dc.date.issued | 2022-09-30 | - |
dc.identifier.citation | M. Kozlenko, M. Kuz, O. Zamikhovska, and L. Zamikhovskyi, "Fault diagnosis of natural gas pumping unit based on machine learning," 6th International Scientific and Practical Conference on Applied Systems and Technologies in the Information Society (AISTIS), V. Pleskach, V. Zosimov, and M. Pyroh, Eds. Taras Shevchenko National University of Kyiv, Kyiv, Ukraine, Sept. 30, 2022, pp. 271-274, doi: 10.5281/zenodo.7409180 | uk_UA |
dc.identifier.other | 10.5281/zenodo.7409180 | - |
dc.identifier.uri | https://zenodo.org/record/7409180 | - |
dc.identifier.uri | http://hdl.handle.net/123456789/14633 | - |
dc.description.abstract | This paper presents a method for fault detection of natural gas pumping unit. It is a very difficult object for diagnosis. A lot of combinations of technical equipment, different operational conditions, and other factors require design and implementation of reliable diagnosis methods. Acoustic signal based fault diagnosis of natural gas pumping units is well known and widely used in a number of applications. Statistical modeling and frequency analysis are among the most popular. In this paper, we share our experience in the use of the classification model based on an artificial multilayered dense feed forward neural network and a deep learning approach for software-implemented diagnosis of a GTK-25-i type of pumping unit. The paper reports the overall accuracy of 0.98 and minimum F1-score of 0.8. This is competitive compared to the latest industry research findings. | uk_UA |
dc.language.iso | en_US | uk_UA |
dc.publisher | Taras Shevchenko National University of Kyiv | uk_UA |
dc.subject | deep learning | uk_UA |
dc.subject | neural network | uk_UA |
dc.subject | fault diagnosis | uk_UA |
dc.subject | fault detection | uk_UA |
dc.subject | natural gas | uk_UA |
dc.subject | pumping unit | uk_UA |
dc.subject | digital signal processing | uk_UA |
dc.subject | classification | uk_UA |
dc.subject | acoustic emission | uk_UA |
dc.subject | vibration | uk_UA |
dc.title | Fault diagnosis of natural gas pumping unit based on machine learning | uk_UA |
dc.type | Article | uk_UA |
Appears in Collections: | Статті та тези (ФМІ) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
AISTIS-2022_kozlenko.pdf | 216.97 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.