Improvement of generalization performance of diagnostic system for drill bit abnormality in rotary percussion drilling with grad-CAM
Abstract 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...
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06796-7 |
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| _version_ | 1849726256439885824 |
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| author | Yuna Nakazawa Natsuo Okada Jo Sasaki Lesego Senjoba Yoko Ohtomo Youhei Kawamura |
| author_facet | Yuna Nakazawa Natsuo Okada Jo Sasaki Lesego Senjoba Yoko Ohtomo Youhei Kawamura |
| author_sort | Yuna Nakazawa |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-c59ca19bb9ad406999cbc4859bbf8c65 |
| institution | DOAJ |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-c59ca19bb9ad406999cbc4859bbf8c652025-08-20T03:10:13ZengSpringerDiscover Applied Sciences3004-92612025-04-017411710.1007/s42452-025-06796-7Improvement of generalization performance of diagnostic system for drill bit abnormality in rotary percussion drilling with grad-CAMYuna Nakazawa0Natsuo Okada1Jo Sasaki2Lesego Senjoba3Yoko Ohtomo4Youhei Kawamura5Division of Sustainable Resources Engineering, Graduate School of Engineering, Hokkaido UniversityDivision of Sustainable Resources Engineering, Graduate School of Engineering, Hokkaido UniversityMMC RYOTEC Co. Ltd.Graduate School of International Resource Sciences, Akita UniversityDivision of Sustainable Resources Engineering, Faculty of Engineering, Hokkaido UniversityDivision of Sustainable Resources Engineering, Faculty of Engineering, Hokkaido UniversityAbstract 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.https://doi.org/10.1007/s42452-025-06796-7Rotary percussion drillConvolutional neural networkGrad-CAMDrill bit abnormalityFrequency filterMining automation |
| spellingShingle | Yuna Nakazawa Natsuo Okada Jo Sasaki Lesego Senjoba Yoko Ohtomo Youhei Kawamura Improvement of generalization performance of diagnostic system for drill bit abnormality in rotary percussion drilling with grad-CAM Discover Applied Sciences Rotary percussion drill Convolutional neural network Grad-CAM Drill bit abnormality Frequency filter Mining automation |
| title | Improvement of generalization performance of diagnostic system for drill bit abnormality in rotary percussion drilling with grad-CAM |
| title_full | Improvement of generalization performance of diagnostic system for drill bit abnormality in rotary percussion drilling with grad-CAM |
| title_fullStr | Improvement of generalization performance of diagnostic system for drill bit abnormality in rotary percussion drilling with grad-CAM |
| title_full_unstemmed | Improvement of generalization performance of diagnostic system for drill bit abnormality in rotary percussion drilling with grad-CAM |
| title_short | Improvement of generalization performance of diagnostic system for drill bit abnormality in rotary percussion drilling with grad-CAM |
| title_sort | improvement of generalization performance of diagnostic system for drill bit abnormality in rotary percussion drilling with grad cam |
| topic | Rotary percussion drill Convolutional neural network Grad-CAM Drill bit abnormality Frequency filter Mining automation |
| url | https://doi.org/10.1007/s42452-025-06796-7 |
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