The Proposal of a Method for Rock Classification Using a Vibration Signal Propagated during the Rotary Drilling Process

Abstract

This research aims to classify rock types based on the vibration signal propagated from the experimental rotary drilling process, where the generated vibration signal is a source of information. Its measurement and processing provide important information about the rock disintegration process, the drilled rock, the drilling tool, and the drilling parameters. For the design of a suitable classification method, several attributes of the vibration signal were calculated for two different signal recording lengths. A cluster dendrogram, an ANOVA test, and a boxplot were used to determine attributes and proper signal length. The classification rule was found using a decision tree, a machine-learning tool. This publication gradually describes the process of creating the classification method and the results of the reliability verification of the proposed classification method. The disintegrated rocks were andesite, granite, limestone, and concrete used as artificial rock. This proposed method classified these three rock types and concrete with a reliability of 100% from a vibration signal record lasting 1/4 s.

Keywords

• rotary drilling
• rock classification
• vibration signal
• cluster dendrogram
• machine learning
• decision tree classifier