The Use of Deep Learning Methods in Diagnosing Rotating Machines Operating in Variable Conditions
Artykuł w czasopiśmie
MNiSW
140
Lista 2021
| Status: | |
| Autorzy: | Pawlik Paweł, Kania Konrad, Przysucha Bartosz |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2021 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 14 |
| Wolumen/Tom: | 14 |
| Numer artykułu: | 4231 |
| Strony: | 1 - 17 |
| Impact Factor: | 3,252 |
| Web of Science® Times Cited: | 6 |
| Scopus® Cytowania: | 8 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | This research was funded by the Polish Ministry of Science and Higher Education, grant number 16.16.130.942, and by the Fund for Science and Research of Lublin University of Technology. |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 13 lipca 2021 |
| Abstrakty: | angielski |
| This paper presents the use of artificial neural networks in diagnosing the technical condition of drive systems operating under variable conditions. The effects of temperature and load variations on the values of diagnostic parameters were considered. An experiment was conducted on a testing rig where a variable load was introduced corresponding to the load of the main gearbox of the bucket wheel excavator. The signals of vibration acceleration on the gearbox body, rotational speed, and current consumption of the drive motor for different values of oil temperature were measured. Synchronous analysis was performed, and the values of order amplitudes and the corresponding values of current, speed, and temperature were determined. Such datasets were the learning vectors for a set of artificial deep learning neural networks. A new approach proposed in this paper is to train the network using a learning set consisting only of data from the efficient system. The responses of the trained neural networks to new data from the undamaged system were performed against the response to data recorded for three damage states: misalignment, unbalance, and simultaneous misalignment and unbalance. As a result, a diagnostic parameter as a normalized measure of the deviation of the network results was developed for the faulted system from the result for the undamaged condition. |
