Fault Diagnosis in a Squirrel-Cage Induction Motor Using Thermal Imaging
Ładowanie...
Data
2023
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Wydawnictwo Politechniki Łódzkiej
Lodz University of Technology Press
Lodz University of Technology Press
Abstrakt
Fault diagnosis is a vivid topic in industrial applications or intelligent
building solutions. One of the well-established techniques involves the measurement
and analysis of current signals. However, this method has several
significant drawbacks, such as the inability to inspect during machinery operation
or the lack of precise information on the malfunction location. This
article proposes a non-invasive method for squirrel-cage induction motor’s
state classification and fault diagnosis. The approach is based on thermal
image analysis that utilizes a compact convolution neural network. In addition,
the gathered and annotated image set, which consists of thermal images
with 640 x 512 pixels resolution, is presented.
Opis
Słowa kluczowe
thermal imaging, fault diagnosis, squirrel-cage induction motor, deep learning, interpretability, termowizja, diagnostyka usterek, silnik indukcyjny klatkowy, głębokie uczenie się, interpretowalność
Cytowanie
Piechocki M., Kraft M., Pajchrowski T., Fault Diagnosis in a Squirrel-Cage Induction Motor Using Thermal Imaging. W: Progress in Polish Artificial Intelligence Research 4, Wojciechowski A. (Ed.), Lipiński P. (Ed.)., Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, s. 37-42, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.4.