Szczegóły publikacji
Opis bibliograficzny
Application of artificial neural networks to determine the length of mining bolts using a self-excited acoustical system / Ireneusz DOMINIK, Krzysztof LALIK, Jan PRZEPIÓRA // W: ICCC 2024 [Dokument elektroniczny] : 25th International Carpathian Control Conference : 22–24 May 2024, Krynica-Zdrój, Poland : proceedings / ed. Andrzej Kot. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2024. — Dod. ISBN: 979-8-3503-5069-2, 979-8-3503-5071-5. — e-ISBN: 979-8-3503-5070-8. — S. [1–4]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [4], Abstr. — Publikacja dostępna online od: 2024-07-01
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 156217 |
|---|---|
| Data dodania do BaDAP | 2024-11-21 |
| Tekst źródłowy | URL |
| DOI | 10.1109/ICCC62069.2024.10569364 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
Abstract
The self-excited acoustical system is a system for indirect determination of stresses in mechanical structures. It uses the phenomenon of self-excitation of the sent acoustic wave in the tested material by the emitter, which is then received by the receiver and fed back to the emitter using positive feedback. The change in strain within the material during the test changes the frequency of the self-excited system, which is indirectly a measure of the stress in the sample. This paper discusses the utilization of data obtained from self-excited system and processed by artificial neural networks to assess the length of mining anchors. The data used were collected during an experimental study at the Wieliczka salt mine in Poland, a live site.
