- Tytuł:
- Induction motor fault classification via entropy and column correlation features of 2D represented vibration data
- Autorzy:
-
Basaran, Murat
Fidan, Mehmet - Powiązania:
- https://bibliotekanauki.pl/articles/1841827.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
- Tematy:
-
entropy
fault diagnosis
support vector machines
wavelet transforms - Opis:
- Due to long-term use under challenging conditions, the sub-elements of induction motors may suffer certain defects over time. Such defects impair the vibration characteristics of the motors in different ways, depending on the type of defect. Therefore, the change in vibration characteristic provides indicators about the fault type and can be used in preventive maintenance strategies to ensure safe operation of the system. In this work, discrete-time vibration data were transformed into 2-dimensional grey-level images and decomposed into individual components by the Wavelet decomposition method. Features based on entropy and column correlation were extracted from these components and used to classify motor faults by using the Support Vector Machine method implemented by using the Sequential Minimal Optimisation algorithm. When the selected classifier is compared with other popular Machine Learning algorithms, it is observed that motor faults are more successfully classified, and these observations are presented in detail with comparative classification performance results.
- Źródło:
-
Eksploatacja i Niezawodność; 2021, 23, 1; 132-142
1507-2711 - Pojawia się w:
- Eksploatacja i Niezawodność
- Dostawca treści:
- Biblioteka Nauki
Artykuł