Classification of wear level of mining tools with the use of fuzzy neural network
Artykuł w czasopiśmie
MNiSW
35
Lista A
| Status: | |
| Autorzy: | Gajewski Jakub, Jedliński Łukasz, Jonak Józef |
| Rok wydania: | 2013 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Wolumen/Tom: | 35 |
| Strony: | 30 - 36 |
| Impact Factor: | 1,589 |
| Web of Science® Times Cited: | 36 |
| Scopus® Cytowania: | 44 |
| Bazy: | Web of Science | Scopus | WoS | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Abstrakty: | angielski |
| In the article, classification test results of the condition of mining tool blades were presented. The tools work as a unit on a multi-tool head. On the research position, signals of mining power for sharp and blunt tools were recorded. Noise of signal power is reduced with the use of discrete wavelet transform in order to emphasize information. Statistical features of signals of mining power were specified, which were later used as entry data for the artificial neural network. Then, the fuzzy neural network, on the basis of calculated signal features, classifies the mining tools in terms of their wear. |
