single-jc.php

JACIII Vol.26 No.3 pp. 367-374
doi: 10.20965/jaciii.2022.p0367
(2022)

Paper:

Accurate Measurement Method of Drilling Depth Based on Multi-Sensor Data Fusion

Yafeng Yao*,**, Ningping Yao**,†, Chunmiao Liang*,**, Hongchao Wei*,**, Haitao Song**, and Li Wang*,**

*China Coal Research Institute
Chaoyang District, Beijing 100013, China

**CCTEG Xi’an Research Institute
No.82, Jinye 1st Rd., Xi’an, Shaanxi 710077, China

Corresponding author

Received:
December 28, 2021
Accepted:
February 28, 2022
Published:
May 20, 2022
Keywords:
drilling depth, real-time measurement, data fusion, adaptive weighted fusion, drilling rig
Abstract

Based on the advanced detection drilling rigs used in underground coal mines, a real-time method of obtaining the depth of drilling is proposed. Displacement sensors are used to measure the stroke of the drilling rig’s feeding device during drilling, and an equation to calculate the depth of drilling is put forward. The same measurements are made by several sensors, improving measurement accuracy and reliability. The final drilling depth is obtained using a multi-sensor data fusion algorithm, combined with the calculation equation. The necessary derivation and calculation processes of the multi-sensor, adaptive weighted fusion algorithm are given. To optimize the integrated result, the weighting coefficients can be found through the algorithm corresponding to each sensor in an adaptive mode to optimize the fusion result. Three kinds of displacement sensors are installed on the feeding device of the drilling rig, and the drilling process is simulated in a laboratory test. The test proves that, compared with the mean method of three sensors, the data obtained by the multi-sensor and adaptive weighted fusion algorithm have the higher accuracy, and the sensor with the least variance in the fusion process has the most significant weighting coefficient. The drilling depth data that are obtained are more accurate than those obtained through the mean method with measurement data from a single sensor. The weighted coefficient of the measurement data is minimal when the measurement accuracy of the sensor suddenly deteriorates, so it has little effect on the measurement results. An experiment verifies this method’s effectiveness and fault tolerance, showing an improvement in measurement accuracy.

Cite this article as:
Y. Yao, N. Yao, C. Liang, H. Wei, H. Song, and L. Wang, “Accurate Measurement Method of Drilling Depth Based on Multi-Sensor Data Fusion,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.3, pp. 367-374, 2022.
Data files:
References
  1. [1] L. Shu, N. Zhu, J. Chen et al., “Theoretical method and technology of precision identification for coal and gas outburst hazard,” J. of China Coal Society, Vol.45, No.5, pp. 1614-1625, 2020.
  2. [2] X. Fang, Y. Geng, M. Wang et al., “Kilometer directional drilling: Simultaneous extraction of coal and gas from a high gas coal seam,” J. of China University of Mining & Technology, Vol.41, No.6, pp. 885-892, 2012.
  3. [3] Z. Shi, Q. Li, and K. Yao, “Development path and key technology analysis of intelligent directional drilling in underground coal mine,” J. of China Coal Society, Vol.45, No.6, pp. 2217-2224, 2020.
  4. [4] R. Gao, L. Hao, B. Liu et al., “Research on underground drill pipe counting method based on improved ResNet network,” Industry and Mine Automation, Vol.46, No.10, pp. 32-37, 2020.
  5. [5] T. Lu, S. Liu, B. Wang et al., “Measurement of deep drilling depth using elastic wave,” Progress in Geophysics, Vol.30, No.5, pp. 2176-2180, 2015.
  6. [6] B. Yan and Q. Zhang, “Design of data acquisition software for drilling depth calibrator based on VC++,” Industrial Instrumentation & Automation, Vol.46, No.3, pp. 28-31, 2017.
  7. [7] T. Fan, Y. Zhang, R. Zhao et al., “Advance detection method of rapid excavation based on borehole TEM intelligent stereo imaging,” J. of China Coal Society, Vol.46, No.2, pp. 578-590, 2021.
  8. [8] N. Yao, Y. Wang, Y. Yao et al., “Progress of drilling technologies and equipment for complicated geological conditions in underground coal mines in China,” Coal Geology & Exploration, Vol.48, No.2, pp. 1-7, 2020.
  9. [9] G. Thonhauser, “Using Real-Time Data for Automated Drilling Performance Analysis,” ErdolErdgas Kohle, Vol.120, No.12, pp. 170-173, 2004.
  10. [10] V. Yurkevich, S. Bykov, and P. Emel’yanov, “Measurement of forming trajectories during drilling,” Measurement Techniques, Vol.49, No.8, pp. 783-786, 2006.
  11. [11] L. Wang, L. Chen, P. Zhang et al., “Design of LWD borehole depth-measuring device,” Coal Geology & Exploration, Vol.45, No.3, pp. 144-146, 2017.
  12. [12] B. Wang, “Design of Depth Measurement System Based on Photoelectric Encoder,” Well Logging Technology, Vol.44, No.4, pp. 343-346,357, 2020.
  13. [13] J. Zhang and X. Wang, “High Precision Measurement Technology of Upward Drilling Depth in Coal Mine,” Safety in Coal Mines, Vol.51, No.7, pp. 132-135, 2020.
  14. [14] H. Li, Y. Hu, Y. Wu et al., “Research on the high precision measurement technique for the underground drilling depth,” Geotechnical Investigation & Surveying, Vol.43, No.12, pp. 80-86, 2015.
  15. [15] J. Gu, M. Meng, A. Cook et al., “Intelligent Sensor Fusion in Robotic Prosthetic Eye System,” J. Adv. Comput. Intell. Intell. Inform., Vol.8, No.3, pp. 313-323, 2004.
  16. [16] Z. Zhu, Z. Xu, Y. Sun et al., “Research on the Risk Evaluation Methods of Water Inrush from Coal Floor Based on Dimensionless Multi-source Information Fusion Technique,” J. of Mining & Safety Engineering, Vol.30, No.6, pp. 911-916, 2013.
  17. [17] H. Zhang, K. Wei, A. Tchameni et al., “Methodology of uncertainty analysis prediction based on multi-well data fusion,” Geosystem Engineering, Vol.21, No.3, pp. 142-150, 2018.
  18. [18] X. Luo, P. Zhan, X. Huang et al., “Rock mass instability prediction method based on weighted D-S evidence theory and multi-domain features fusion,” J. of China Coal Society, Vol.45, No.10, pp. 3446-3452, 2020.
  19. [19] N. Zheng, Y. Du, and Q. Bai, “Robot Navigation Algorithm Based on Sensor Technology and Iterative Maximum a Posteriori stimation,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.2, pp. 282-286, 2019.
  20. [20] H. Liu, H. Wang, and X. Li, “Design of Fusion Recognition System for Space Objects Based Heterogeneous Sensors,” Signal Processing, Vol.26, No.2, pp. 203-207, 2010.
  21. [21] Y. Tang, D. Zhou, S. Xu et al., “A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion,” Sensors, Vol.17, No.4, pp. 928-943, 2017.
  22. [22] Z. Li, R. Chen, and B. Zhang, “Study of adaptive weighted estimate algorithm of congeneric multi-sensor data fusion,” J. of Lanzhou University of Technology, Vol.32, No.4, pp. 78-82, 2006.
  23. [23] Z. Zyada, Y. Kawai, T. Matsuno et al., “Fuzzy Sensor Fusion for Humanitarian Demining,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.7, pp. 735-744, 2007.
  24. [24] H. Wang and Q. Zhang, “Dynamic identification of coal-rock interface based on adaptive weight optimization and multi-sensor information fusion,” Information Fusion, Vol.51, pp. 114-128, 2019.
  25. [25] S. Xiao, Z. Zhang, and X. Huang, “Multi-channel fiber-optic displacement sensor based on data fusion,” Optics and Precision Engineering, Vol.21, No.11, pp. 2764-2770, 2013.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Dec. 02, 2022