Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot
Artykuł w czasopiśmie
MNiSW
100
Lista 2021
Status: | |
Autorzy: | Wang Dan-Feng, Guo Yu, Wu Xing, Na Jing, Litak Grzegorz |
Dyscypliny: | |
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Rok wydania: | 2020 |
Wersja dokumentu: | Drukowana | Elektroniczna |
Język: | angielski |
Numer czasopisma: | 3 |
Wolumen/Tom: | 10 |
Numer artykułu: | 932 |
Strony: | 1 - 12 |
Impact Factor: | 2,679 |
Web of Science® Times Cited: | 28 |
Scopus® Cytowania: | 32 |
Bazy: | Web of Science | Scopus |
Efekt badań statutowych | NIE |
Finansowanie: | This research was funded by the National Natural Science Foundation of China, grant number 51675251. |
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: | 31 stycznia 2020 |
Abstrakty: | angielski |
Recurrence-plot (RP) analysis is a graphical tool to visualize and analyze the recurrence of nonlinear dynamic systems. By combining the advantages of the RP and a convolutional neural network (CNN), a fault-classification scheme for planetary gear sets is proposed in this paper. In the proposed approach, a vibration is first picked up from the planetary-gear test rig and converted into an angular-domain quasistationary signal through computed order tracking to eliminate the frequency blur caused by speed fluctuations. Then, the signal in the angular domain is divided into several segments, and each segment is processed by the RP to constitute the training sample. Moreover, a two-dimensional CNN model was developed to adaptively extract faulty features. Experiments on a planetary-gear test rig with four conditions under three operating speeds were carried out. The results of measured vibration demonstrated the validity of CNN and recurrence plot analysis for the fault classification of planetary-gear sets. |