Asynchronous motor fault detection using machine learning algorithms
- Authors: Sereda E.G.1, Solovyov A.S.2,3
-
Affiliations:
- LLC “RackFork”
- Emperor Alexander I St. Petersburg State Transport University
- JSC “Power machines”
- Issue: Vol 11, No 2 (2025)
- Pages: 261-272
- Section: Original studies
- URL: https://ogarev-online.ru/transj/article/view/311285
- DOI: https://doi.org/10.17816/transsyst682000
- ID: 311285
Cite item
Full Text
Abstract
BACKGROUND. Machine learning methods are effective advanced means ensuring the operability of various engineering systems, including test systems. As statistics on faults accumulate, test systems based on machine learning algorithms provide higher prediction accuracy and do not require expensive test equipment and skilled personnel.
AIM. To develop a test system capable of both determining the fault and assessing its extent with high accuracy.
MATERIALS AND METHODS. The subject of the study is a three-phase asynchronous motor with a squirrel cage rotor; machine learning methods are used to achieve the goal.
RESULTS. Using the example of interturn faults in the stator winding, the authors demonstrate that it is possible to detect the fault and its extent even at the initial stage (with a few short-circuited turns) with an accuracy of at least 95%.
CONCLUSION. Machine learning methods allow to develop effective and affordable test systems that are versatile, highly accurate, and do not require skilled personnel.
Full Text
##article.viewOnOriginalSite##About the authors
Evgeny G. Sereda
LLC “RackFork”
Email: evgeniy.sereda@rackfork.ru
SPIN-code: 4284-3319
Cand. Sci. (Tech.), Senior specialist
Russian Federation, St. PetersburgAndrey S. Solovyov
Emperor Alexander I St. Petersburg State Transport University; JSC “Power machines”
Author for correspondence.
Email: vgvhyjh@mail.ru
ORCID iD: 0009-0001-2408-1840
SPIN-code: 1594-5049
Post graduate student, test engineer
Russian Federation, St. Petersburg; St. PetersburgReferences
- Kotelents NF, Akimova NA, Antonov MV. Ispytania, ekspluatatsia i remont elektricheskikh mashin. Moscow: Akademia; 2003. (In Russ.)
- Dorokhina ES, Goldovskaya AA, Girnik AS. A Way To Predict The Residual Lifetime Of Induction Traction Electric Motors. Journal of Machinery Manufacture and Reliability. 2019;48(4):336-339. (Russ., Engl.) doi: 10.1134/S0235711919040060
- Vorobiev VE, Kucher VIa. Prognozirovanie sroka sluzhby elektricheskikh mashin. St.-Petersburg: SZTU, 2004. 56 p. (In Russ.).
- Ryzhova EL, Kim KK, Ivanov SN. Evaluation оf Traction Motor Reliability Based On The State Of Winding Electrical Insulation. Učenye zapiski Komsomol’skogo-na-Amure gosudarstvennogo tehničeskogo universiteta. 2023; 5(69):8-14. (In Russ.). doi: 10.17084/20764359-2023-69-8
- Miasoedova MA, Mamonova LG. Analiz sovremennykh metodov diagnostiki elektrooborudovania. In: Proceedings of the 2nd All-Russian Scientific and Practical Conference “Technologii, mashiny i oborudovanie dlya agropromyshlennogo kompleksa”. 2024 June 5. Kursk: Universitetskaya Kniga. 2024:81-84. (In Russ.).
- Koroleva EB, Kurmashev SM, Kim KK, Tkachuk AA, Kuznetsov AA. Devices for express diagnostics of power semiconductor devices and semiconductor converters. Omsk Scientific Bulletin. 2023;2(186):119-125. (Russ., Engl.) doi: 10.25206/1813-8225-2023-186-119-125
- Siddique A, Yadava G, Singh B. A Review of Stator Fault Monitoring Techniques of Induction Motors. IEEE transactions on energy conversion. 2005;20(1):106-114. doi: 10.1109/TEC.2004.837304
- Villada F, Cadavid D, Munoz N, et al. Fault Diagnosis in Induction Motors Fed by PWM Inverters. In: 4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2003. SDEMPED; 2003:229-234. doi: 10.1109/DEMPED.2003.1234578
- Ryzhova EL, Osipov VYu. The use of technical means for diagnosing traction engines of rolling stock. Modern Transportation Systems and Technologies. 2024;10(4):556-576. doi: 10.17816/transsyst635142557
- Salnikov SV, Solodkiy EM, Vishnyakov DD, et al. Diagnostika asinhronnogo dvigatelia na osnove mashinnogo obuchenia. In: Proceedings of the XXVI International Conference on soft Computing and Measurements SCM-2023. 2023 May 24-26. St. Petersburg: SPBGETU “LETI”; 2023.
- Khalyasmaa A. Machine Learning as a Tool of High-Voltage Electrical Equipment Lifecycle Control Enhancement. Proceedings of Irkutsk State Technical University. 2020;24(5):1093-1094.] doi: 10.21285/1814-3520-2020-5-1093-1104.
- Korobeinikov AB, Sarvarov AS. Analysis Of Existing Methods for Diagnostics of Electric Motors and Perspectives of their Development. Electrotechnical Systems and Complexes. 2015;26(1):4-9. doi: 10.18503/2311-8318
- Breiman L. Random Forests. Machine Learning. 2001;45(1):5-32. doi: 10.1023/A:1010933404324
- Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM; 2016:785-794.
- Flakh P. Mashinnoe obuchenie. Nauka i iskusstvo postroenia algoritmov kotorye izvlekaut znznia iz dannyh. Moscow: DMK Press; 2015. (In Russ.).
- Kugaevskih AV, Muromtsev DI, Kirsanova OV. Klassicheskie Metody Mashinnogo Obuchenia. St.-Petersburg: Universitet ITMO; 2022. (In Russ.)
Supplementary files
