Fault Diagnosis in a Squirrel-Cage Induction Motor Using Thermal Imaging
[ 1 ] Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ 2 ] Instytut Robotyki i Inteligencji Maszynowej, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ SzD ] doktorant ze Szkoły Doktorskiej | [ P ] pracownik
[2.2] Automatyka, elektronika, elektrotechnika i technologie kosmiczne
2023
rozdział w monografii naukowej / referat
angielski
- thermal imaging
- fault diagnosis
- squirrel-cage induction motor
- deep learning
- interpretability
EN 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.
37 - 42
Licencja Politechniki Łódzkiej
otwarte repozytorium
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w momencie opublikowania
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