Implementation of MLP-based Classifier of Current Sensor Faults in Vector-Controlled Induction Motor Drive

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dc.contributor.authorTeler, Krystian
dc.contributor.authorSkowron, Maciej
dc.contributor.authorOrlowska-Kowalska, Teresa
dc.contributor.editorOrlowska-Kowalska Teresa
dc.contributor.organizationPolitechnika Wrocławska, Wydział Elektryczny, Katedra Maszyn, Napędów i Pomiarów Elektrycznych
dc.date.accessioned2025-02-17T11:22:18Z
dc.date.available2025-02-17T11:22:18Z
dc.date.issued2024-04-04
dc.descriptionPaper NO. TII-23-3874, sent for review on 6 October 2023, accepted 12 November 2023.
dc.description.abstractThis article deals with the neural networks (NN)-based classification of current sensor (CS) faults in an induction motor drive system designed according to the idea of fault-tolerant control (FTC). The CS-FTC system ensures continued system functionality, even after a failure has occurred, not only through its ability to detect and isolate faults but also through its ability to compensate for them. The article focuses on the development of the technical implementation of a CS faults classifier based on NN in considering the problem. The proposed method uses the principle structure of a multilayer perceptron (MLP) based on single samples of the stator phase current signal. The tests demonstrate techniques that allow the development of NN input vectors while eliminating the constraints resulting from the machine's operating conditions (load change, rotational speed change). In addition, the possibility of optimizing the input vector of the network in terms of the analyzed damages is demonstrated by using harmonic analysis. The article shows the possibility of full use of the mathematical model in the practical implementation of diagnostic systems implemented on a real object. MLP aims to determine the CS fault category (no fault, gain change, offset, saturation, open circuit) and fault localization (phase A or B). The experimental verification on a laboratory stand with an induction motor of 1.1 kW confirmed the efficiency of the proposed method in 97.2% of the cases.en
dc.description.sponsorshipNarodowe Centrum Nauki, Polska, grant No. 2021/41/B/ST7/02971
dc.identifier.citationK. Teler, M. Skowron and T. Orłowska-Kowalska, "Implementation of MLP-Based Classifier of Current Sensor Faults in Vector-Controlled Induction Motor Drive," in IEEE Transactions on Industrial Informatics, vol. 20, no. 4, pp. 5702-5713, April 2024, doi: 10.1109/TII.2023.3336348.
dc.identifier.doi10.1109/TII.2023.3336348
dc.identifier.urihttps://open.icm.edu.pl/handle/123456789/25449
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartofseries20; 4
dc.rightsAttribution-NoDerivatives 4.0 Międzynarodoween
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/
dc.sourceIEEE Transactions on Industrial Electronics
dc.subjectcurent sensor faulten
dc.subjectfault type classificationen
dc.subjectinduction motor driveen
dc.subjectmultilayer perceptron (MLP)en
dc.subjecttraining patternsen
dc.titleImplementation of MLP-based Classifier of Current Sensor Faults in Vector-Controlled Induction Motor Driveen
dc.title.alternativeImplementacja klasyfikatora uszkodzeń czujników prądu opartego na MLP w napędzie silnika indukcyjnego sterowanego wektorowopl
dc.typearticle
dc.type.versionacceptedVersion
person.identifier.orcidTeler, Krystian [0000-0001-5790-6856]
person.identifier.orcidSkowron, Maciej [0000-0002-0488-8567]
person.identifier.orcidOrlowska-Kowalska, Teresa [0000-0002-4592-5336]
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