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<p>Rotary percussion drills, specifically top hammer types, are essential in resource extraction and exploration. These drills frequently suffer damage at the drill bit, necessitating effective decision-making to address issues. Traditionally, on-site operators relied on intuition, but recent advancements promote automated diagnostic systems to enhance safety and failure detection in challenging mining environments, such as those with high dust levels and intense vibrations. This study uses Convolutional Neural Networks (CNN) to develop an automated diagnostic system for detecting drill bit abnormalities. Previous models classified five distinct bit conditions but faced challenges in generalizing performance due to variations in the frequency bands used for judgments. This study refines the approach by simplifying the system to detect two states: normal and abnormal. Using Gradient-weighted Class Activation Mapping (Grad-CAM), the system identifies critical frequency bands, enabling a unified frequency filter. By training the model to recognize differences in rock types, it adapts to untrained drill bit types, achieving an accuracy of 83.3%, significantly higher than the 44.8% in previous models. This innovation allows the system to generalize across different rock and bit conditions, improving drilling efficiency and minimizing downtime in diverse environments.</p>

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Improvement of generalization performance of diagnostic system for drill bit abnormality in rotary percussion drilling with grad-CAM

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月12日

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  • Yuna Nakazawa,
  • Natsuo Okada,
  • Jo Sasaki,
  • Lesego Senjoba,
  • Yoko Ohtomo,
  • Youhei Kawamura

摘要

Rotary percussion drills, specifically top hammer types, are essential in resource extraction and exploration. These drills frequently suffer damage at the drill bit, necessitating effective decision-making to address issues. Traditionally, on-site operators relied on intuition, but recent advancements promote automated diagnostic systems to enhance safety and failure detection in challenging mining environments, such as those with high dust levels and intense vibrations. This study uses Convolutional Neural Networks (CNN) to develop an automated diagnostic system for detecting drill bit abnormalities. Previous models classified five distinct bit conditions but faced challenges in generalizing performance due to variations in the frequency bands used for judgments. This study refines the approach by simplifying the system to detect two states: normal and abnormal. Using Gradient-weighted Class Activation Mapping (Grad-CAM), the system identifies critical frequency bands, enabling a unified frequency filter. By training the model to recognize differences in rock types, it adapts to untrained drill bit types, achieving an accuracy of 83.3%, significantly higher than the 44.8% in previous models. This innovation allows the system to generalize across different rock and bit conditions, improving drilling efficiency and minimizing downtime in diverse environments.