Monitoring of drill bit wear using sound and vibration signals analysis recorded during rock drilling operations

被引:2
作者
Kalhori, Hamid [1 ]
Bagherpour, Raheb [1 ]
Tudeshki, Hossein [2 ]
机构
[1] Isfahan Univ Technol, Dept Min Engn, Esfahan 8415683111, Iran
[2] Tech Univ Clausthal, Dept Surface Min & Int Min, Inst Min, POB 38678, Clausthal Zellerfeld, Germany
关键词
Drilling; Bit; Sound; Vibration; Wear; Condition monitoring; TOOL WEAR; PREDICTION; FEATURES;
D O I
10.1007/s40808-023-01901-4
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The drill bit has significant importance as a tool for breaking rocks in many industries such as mining, petroleum, and civil engineering. Its durability and effectiveness directly impact crucial aspects like drilling expenses, time efficiency, productivity, and safety. Therefore, it is vital to comprehend the rock-bit behavior, including the wear of the bit at the bottom of the well. Consequently, there is a need to establish a model for the purpose of assessing the level of bit wear. This paper introduces a novel approach for examining the correlation between drilling signals and the state of bit wear in drilling operations conducted at a laboratory scale. In this work, an experimental study of the rock drilling process was performed on 30 different rock samples, and sound and vibration signals were collected during the drilling process. Consequently, a range of signal processing techniques was utilized to extract distinctive characteristics from the vibro-acoustic signals in order to indicate the degree of bit wear. Spectral analysis of the signals revealed that the magnitude of rock drilling noise and bit vibration features is influenced by the progressive wear of the bits, which could be utilized as an indicator of the wear conditions of the bits during the drilling process. The findings of this study indicate that the recorded vibro-acoustic signals serve as a comprehensive source of information, providing insights into the condition of the rock drilling bits. The provided information possesses the potential to serve as the foundation for an industrial bit condition monitoring system capable of identifying and notifying the user about excessive wear.
引用
收藏
页码:2611 / 2659
页数:49
相关论文
共 45 条
[31]   Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling [J].
Achyuth Kothuru ;
Sai Prasad Nooka ;
Rui Liu .
The International Journal of Advanced Manufacturing Technology, 2018, 95 :3797-3808
[32]   The milling cutter-wear monitoring based on time-frequency analysis of milling sound signals [J].
Ai, Changsheng ;
Dong, Quancheng ;
Sun, Xuan .
WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, :5810-+
[33]   Multiple regression model for prediction of rock properties using acoustic frequency during core drilling operations [J].
Kumar, Ch Vijaya ;
Vardhan, Harsha ;
Murthy, Ch S. N. .
GEOMECHANICS AND GEOENGINEERING-AN INTERNATIONAL JOURNAL, 2020, 15 (04) :297-312
[34]   A Deep Feature Learning Method for Drill Bits Monitoring Using the Spectral Analysis of the Acoustic Signals [J].
Vununu, Caleb ;
Moon, Kwang-Seok ;
Lee, Suk-Hwan ;
Kwon, Ki-Ryong .
SENSORS, 2018, 18 (08)
[35]   Tool Wear Monitoring of Multipoint Cutting Tool using Sound Signal Features Signals with Machine Learning Techniques [J].
Ravikumar, S. ;
Ramachandran, K. I. .
MATERIALS TODAY-PROCEEDINGS, 2018, 5 (11) :25720-25729
[36]   The Assessment of Material Characteristic using Vibration Signal Analysis during Drilling Process [J].
Inayatullah, Othman ;
Hamid, Faizal ;
Hon, Hew Wei ;
Jamaludin, Nordin ;
Abdullah, Shahrum .
ADVANCED MATERIALS, MECHANICS AND INDUSTRIAL ENGINEERING, 2014, 598 :3-7
[37]   The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation [J].
Mohamed Lamine Bouhalais ;
Mourad Nouioua .
The International Journal of Advanced Manufacturing Technology, 2021, 115 :2989-3001
[38]   The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation [J].
Bouhalais, Mohamed Lamine ;
Nouioua, Mourad .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2021, 115 (9-10) :2989-3001
[39]   Fractal analysis implementation for tool wear monitoring based on cutting force signals during CFRP/titanium stack machining [J].
Maryam Jamshidi ;
Xavier Rimpault ;
Marek Balazinski ;
Jean-François Chatelain .
The International Journal of Advanced Manufacturing Technology, 2020, 106 :3859-3868
[40]   Fractal analysis implementation for tool wear monitoring based on cutting force signals during CFRP/titanium stack machining [J].
Jamshidi, Maryam ;
Rimpault, Xavier ;
Balazinski, Marek ;
Chatelain, Jean-Francois .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2020, 106 (9-10) :3859-3868