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.
Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies
Informacja
SZANOWNI CZYTELNICY!
UPRZEJMIE INFORMUJEMY, ŻE BIBLIOTEKA FUNKCJONUJE W NASTĘPUJĄCYCH GODZINACH:
Wypożyczalnia i Czytelnia Główna: poniedziałek – piątek od 9.00 do 19.00