Version 1
: Received: 28 June 2023 / Approved: 29 June 2023 / Online: 29 June 2023 (11:07:47 CEST)
How to cite:
Stehlíková, B.; Bogdanovská, G.; Flegner, P.; Frančáková, R.; Drančák, L. Rock Classification Using a Vibration Signal in the Process of Rotary Drilling. Preprints2023, 2023062097. https://doi.org/10.20944/preprints202306.2097.v1
Stehlíková, B.; Bogdanovská, G.; Flegner, P.; Frančáková, R.; Drančák, L. Rock Classification Using a Vibration Signal in the Process of Rotary Drilling. Preprints 2023, 2023062097. https://doi.org/10.20944/preprints202306.2097.v1
Stehlíková, B.; Bogdanovská, G.; Flegner, P.; Frančáková, R.; Drančák, L. Rock Classification Using a Vibration Signal in the Process of Rotary Drilling. Preprints2023, 2023062097. https://doi.org/10.20944/preprints202306.2097.v1
APA Style
Stehlíková, B., Bogdanovská, G., Flegner, P., Frančáková, R., & Drančák, L. (2023). Rock Classification Using a Vibration Signal in the Process of Rotary Drilling. Preprints. https://doi.org/10.20944/preprints202306.2097.v1
Chicago/Turabian Style
Stehlíková, B., Rebecca Frančáková and Ladislav Drančák. 2023 "Rock Classification Using a Vibration Signal in the Process of Rotary Drilling" Preprints. https://doi.org/10.20944/preprints202306.2097.v1
Abstract
The paper proposes a rock classification method using the vibration signal of the rotary drilling process. This proposed method classifies four rocks with a reliability of 100 %, from the vibration signal record of a lasting 1/4 second. For the design of a suitable classification method, several attributes of the vibration signal were calculated for two different signal recording lengths. Cluster dendrogram, ANOVA test, and boxplot were used to determine useful attributes and proper signal length. The classification rule was found using a decision tree, machine learning tool. The publication gradually describes the process of creating the classification method and the results of the reliability verification of the proposed classification method. Rock disintegration by rotary drilling was carried out under standardized experimental conditions. The disintegration rocks were andesite, granite, limestone, and concrete.
Keywords
rotary drilling, rock classification, vibration signal, cluster dendrogram, machine learning, the decision tree classifier
Subject
Engineering, Mining and Mineral Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
