Optimal selection of signal features in the diagnostics of mining head tools condition
Artykuł w czasopiśmie
MNiSW
140
Lista 2021
| Status: | |
| Autorzy: | Jedliński Łukasz, Gajewski Jakub |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2019 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Wolumen/Tom: | 84 |
| Strony: | 451 - 460 |
| Impact Factor: | 4,45 |
| Web of Science® Times Cited: | 15 |
| Scopus® Cytowania: | 16 |
| Bazy: | Web of Science | Scopus | ScienceDirect |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| The fact that mining tools must be replaced earlier than other parts to prevent machine failure and, consequently, its stoppage results in a significant increase in machinery operating costs. The replacement of cutting tools usually depends on the operator’s subjective decision rather than on specific parameters of the machine. This paper presents the numerical results of a study investigating the suitability of selected feature selection methods for providing information about the cutters’ condition. The feature selection methods used in this study to determine the resistance signal of a mining head allow us to select signal features that are variables of the classifying neural systems. A detailed survey of the literature on the subject confirms that such study is necessary. The development and implementation of an effective system for measuring wear of a cutting tool is of a great practical significance. The method proposed in this study can be implemented not only in underground mining but in other branches of the industry, too. |
