- Tytuł:
- Research on vibration-based early diagnostic system for excavator motor bearing using 1-D CNN
- Autorzy:
-
Yandagsuren, Dorjsuren
Kurauchi, Tatsuki
Toriya, Hisatoshi
Ikeda, Hajime
Adachi, Tsuyoshi
Kawamura, Youhei - Powiązania:
- https://bibliotekanauki.pl/articles/2201430.pdf
- Data publikacji:
- 2023
- Wydawca:
- Główny Instytut Górnictwa
- Tematy:
-
bearing diagnosis
electric motor
vibration analysis
signal processing
1-D CNN
diagnostyka łożysk
silnik elektryczny
analiza drgań
przetwarzanie sygnałów - Opis:
- In mining, super-large machines such as rope excavators are used to perform the main mining operations. A rope excavator is equipped with motors that drive mechanisms. Motors are easily damaged as a result of harsh mining conditions. Bearings are important parts in a motor; bearing failure accounts for approximately half of all motor failures. Failure reduces work efficiency and increases maintenance costs. In practice, reactive, preventive, and predictive maintenance are used to minimize failures. Predictive maintenance can prevent failures and is more effective than other maintenance. For effective predictive maintenance, a good diagnosis is required to accurately determine motor-bearing health. In this study, vibration-based diagnosis and a one-dimensional convolutional neural network (1-D CNN) were used to evaluate bearing deterioration levels. The system allows for early diagnosis of bearing failures. Normal and failure-bearing vibrations were measured. Spectral and wavelet analyses were performed to determine the normal and failure vibration features. The measured signals were used to generate new data to represent bearing deterioration in increments of 10%. A reliable diagnosis system was proposed. The proposed system could determine bearing health deterioration at eleven levels with considerable accuracy. Moreover, a new data mixing method was applied.
- Źródło:
-
Journal of Sustainable Mining; 2023, 22, 1; 65--80
2300-1364
2300-3960 - Pojawia się w:
- Journal of Sustainable Mining
- Dostawca treści:
- Biblioteka Nauki
Artykuł