Rotor Fault Detection of the Converter-Fed Induction Motor using General Regression Neural Networks

• Autorzy: Kamiński M. , Kowalski C. T.

Warianty tytułu

PL

Detekcja uszkodzeń wirnika silnika indukcyjnego zasilanego z przekształtnika przy wykorzystaniu regresyjnych sieci neuronowych

• Języki publikacji: EN

Abstrakty

EN

This paper deals with the application of the General Regression Neural Networks as the rotor fault detectors of the converter-fed induction motors. The major advantages of GRNN application in the considered task are simplified design process and high quality of data classification. Specific fault symptoms of the rotor damages included in the measured stator current spectrum are proposed as elements of the input vectors of the GRNN-based detector. Diagnostic results obtained by the proposed neural detector of rotor faults are demonstrated.

PL

W artykule przedstawiono zastosowanie regresyjnych sieci neuronowych (GRNN) jako detektorów uszkodzeń wirnika silnika indukcyjnego zasilanego z przekształtnika częstotliwości. Najistotniejszymi zaletami zastosowania modeli GRNN w opisywanej aplikacji są: uproszczony proces projektowania oraz wysoka precyzja klasyfikacji danych. Zaprezentowano również szczegóły związane z generowaniem przesłanek uszkodzeń, będących elementami wektora wejściowego sieci neuronowej.

Słowa kluczowe

EN

induction motor; rotor bar faults; fault detection; general regression neural networks

PL

silnik indukcyjny; uszkodzenia prętów wirnika; regresyjne sieci neuronowe

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 12a
• Strony: 71--77
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Kamiński M.

autor

Kowalski C. T.

• Politechnika Wrocławska, Instytut Maszyn, Napędów i Pomiarów Elektrycznych, ul. Smoluchowskiego 19, 50-372 Wrocław,

Bibliografia

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