Development of diagnostic system for the state of electric drives of food enterprise

• Autorzy: Lutska Nataliia , Zaiets Nataliia , Vlasenko Lidiia

Identyfikatory

DOI

10.15199/48.2024.02.33

Warianty tytułu

PL

Opracowanie systemu diagnostyki stanu napędów elektrycznych przedsiębiorstwa spożywczego

• Języki publikacji: EN

Abstrakty

EN

The paper presents a system for diagnosing the state of electric drives based on a machine learning classification model. At the initial stage, the behavior of the diagnostic system and external subsystems was modelled based on UML diagrams. The input data for the model for diagnosing the state of electric drives is a set of data, which, as a rule, are measured in food enterprises. The deterioration of qualitative classification scores with an incomplete feature vector was studied when using different training methods.

PL

W artykule przedstawiono system diagnozowania stanu napędów elektrycznych oparty na modelu klasyfikacyjnym uczenia maszynowego. W początkowej fazie zamodelowano zachowanie systemu diagnostycznego oraz podsystemów zewnętrznych w oparciu o diagramy UML. Danymi wejściowymi do modelu diagnozowania stanu napędów elektrycznych jest zbiór danych, które z reguły są mierzone w przedsiębiorstwach spożywczych. Zbadano pogorszenie jakościowych wyników klasyfikacji z niepełnym wektorem cech przy użyciu różnych metod szkoleniowych.

Słowa kluczowe

EN

electric drive; system for diagnosing; quantitative estimate

PL

napęd elektryczny; system diagnozowania; szacunek ilościowy

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2024
• Tom: R. 100, nr 2
• Strony: 164--167
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Lutska Nataliia

• National University of Food Technologies, Volodimirska str., 68, 01601, Kyiv, Ukraine

• https://orcid.org/0000-0001-8593-0431

autor

Zaiets Nataliia

• National University of Life and Environmental Sciences of Ukraine, Defense Heroes str., 15, 03041, Kyiv, Ukraine

• https://orcid.org/0000-0001-5219-2081

autor

Vlasenko Lidiia

• State University of Trade and Economics, Kyoto str., 19, 02156, Kyiv, Ukraine

• https://orcid.org/0000-0002-2003-6313

Bibliografia

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• [3] Mantravadi S., Møller C., Chen L.I., Schnyder R., Design choices for next-generation IIoT-connected MES/MOM: An empirical study on smart factories, Robotics and Computer-Integrated Manufacturing, 73 (2022), 102225
• [4] Mantravadi S., Møller C., An Overview of Next-generation Manufacturing Execution Systems. Procedia Manufacturing, 30 (2019), 588-595
• [5] Straka L., Corny I., Krehel R., Evaluation of capability of measuring device on the basis of diagnostics, Applied Mechanics and Materials, 308 (2013), 69-74
• [6] Duer S., Rokosz K., Zajkowski K., Bernatowicz D., Ostrowski A., Woźnia, M., Iqbal A., Intelligent Systems Supporting the Use of Energy Devices and Other Complex Technical Objects, Energies, 15 (2022), No. 17, 1-6
• [7] Sajewicz D., Łaguna W., Chmielak W. Detection of fault events in electric motor based on analysis of data from fault recorders, Przegląd Elektrotechniczny, 99 (2023), No. 1, 207-210
• [8] Ribeiro Junior R.F., de Almeida F. A., Gomes G. F., Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks, Neural Computing and Applications, 32 (2020), 15171-15189
• [9] Zhongming Y.E., Bin W.U., A review on induction motor online fault diagnosis, Proceedings IPEMC 2000. Third International Power Electronics and Motion Control Conference, (2000), No. 3, 1353-1358
• [10] Aimer A.F., Boudinar A.H., Khodja M.A., Bendiabdellah A., Frequency resolution improvements in induction motor fault diagnosis: Experimental validation, Przeglad Elektrotechniczny, 97 (2021), No. 11, 69-73
• [11] Jiang T., Gradus J.L. Supervised machine learning: a brief primer, Behavior Therapy, 51 (2020), No. 5, 675-687
• [12] Zheng M., You S., Huang L., Wang F., Qian C., Xu, C., Simmatch: Semi-supervised learning with similarity matching, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2022), 14471-14481
• [13] Krishnan R., Rajpurkar P., Topol E.J., Self-supervised learning in medicine and healthcare, Nature Biomedical Engineering, 6 (2022), No. 12, 1346-1352
• [14] Kang Z., Catal C., Tekinerdogan B., Machine learning applications in production lines: A systematic literature review, Computers and Industrial Engineering, 149 (2020), 106773
• [15] Wuest T., Weimer D., Irgens C., Thoben K.D., Machine learning in manufacturing: Advances, challenges, and applications, Production and Manufacturing Research, 4 (2016), 23-45
• [16] Bulusu S., Kailkhura B., Li B., Varshney P.K., Song D., Anomalous Instance Detection in Deep Learning: A Survey, arXiv:2003.06979, (2020)
• [17] Vlasenko L., Lutska N., Zaiets N., Korobiichuk I., Hrybkov S., Core Ontology for Describing Production Equipment According to Intelligent Production, Appl. Syst. Innov., 5 (2022), No. 5, 98
• [18] Lutska N., Vlasenko L., Zaiets N., Lysenko V., Modeling the Productivity of a Sugar Factory using Machine Learning Methods, IEEE 17th International Conference on Computer Science and Information Technologies, (2022), 34-43

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).