Signal Analysis of the Armature Rotation Irregularities in the Traction Electric Motor by Unsupervised Anomaly Detection Methods
| dc.contributor.author | Bodnar, Borys Ye. | en |
| dc.contributor.author | Ochkasov, Oleksandr B. | en |
| dc.contributor.author | Serdiuk, Volodymyr N. | en |
| dc.contributor.author | Ochkasov, Mykhailo | en |
| dc.date.accessioned | 2023-02-28T17:47:29Z | |
| dc.date.available | 2023-02-28T17:47:29Z | |
| dc.date.issued | 2022 | |
| dc.description | B. Bodnar: ORCID 0000-0002-3591-4772; O. Ochkasov: ORCID 0000-0002-7719-7214; V. Serdiuk: ORCID 0000-0003-2337-3478; M. Ochkasov: ORCID 0000-0002-9198-0758 | en |
| dc.description.abstract | ENG: Reducing the cost of maintenance and repair of vehicles is possible due to the early detection of malfunctions development and more complete use of the equipment resource. The introduction of contemporary information technologies allows real-time monitoring of the technical parameters for the equipment. Anomaly detection methods are used to process monitoring results. The paper presents the results of using the Unsupervised Anomaly Detection methods to analyze the signal of the rotation velocity irregularity of the armature shaft in the traction electric motor of a locomotive. When analyzing signals corresponding to faulty electric motors, anomalous components were identified. As the example of analyzing the signal of an electric motor with an increased radial clearance, the possibility of detecting the development of a malfunction at an early stage has been confirmed. The conducted research confirms the possibility of using anomaly search methods to control the technical condition of the traction electric motor in a locomotive during bench tests. | en |
| dc.description.sponsorship | Taras Shevchenko National University of Kyiv, Kyiv, Ukraine | en |
| dc.identifier.citation | Bodnar B., Ochkasov O., Serdiuk V., Ochkasov M. Signal Analysis of the Armature Rotation Irregularities in the Traction Electric Motor by Unsupervised Anomaly Detection Methods. Transport Means 2022 : Proc. of the 26th Intern. Sci. Conf. (05–07 Oct., 2022, Kaunas, Lithuania). Kaunas, 2022. Pt. II. P. 761–766. | en |
| dc.identifier.uri | http://eadnurt.diit.edu.ua/jspui/handle/123456789/16538 | |
| dc.identifier.uri | https://transportmeans.ktu.edu | |
| dc.language.iso | en | |
| dc.publisher | Kaunas University of Technology, Lithuania | en |
| dc.subject | technical monitoring | en |
| dc.subject | electric motor | en |
| dc.subject | unsupervised anomaly detection | en |
| dc.subject | DBSCAN | en |
| dc.subject | OneClassSVM | en |
| dc.subject | Elliptic Envelop | en |
| dc.subject | Isolation Forest | en |
| dc.subject | КЛ | uk_UA |
| dc.title | Signal Analysis of the Armature Rotation Irregularities in the Traction Electric Motor by Unsupervised Anomaly Detection Methods | en |
| dc.type | Article | en |