Intelligent Identification of Coal-Rock Type Based on Boring Parameters of Dig Windlass and XGBoost

DOI

10.2991/978-94-6463-022-0_15How to use a DOI?

Keywords

Boring parameters; Coal and rock identification; XGBoost; Dig windlass

Abstract

Coal is an important natural resource in China and plays an essential role in the development of industry and national economy. To realize unmanned mining, it is necessary to identify coal-rock type of working face accurately and efficiently. As the photographing is interfered by water mist, dust, air flow, lighting, vibration and other factors, the accuracy of image feature recognition methods are seriously affected. Therefore, this paper proposes an intelligent identification method based on boring parameters of dig windlass and XGBoost algorithm. Firstly, the coupling relationship between machine parameters recorded by dig windlass was analysed to remove a large number of redundant parameters, which reduces 22.7% training time of the model and 63.8% identification time. Secondly, remove the data recorded by the dig windlass under abnormal working conditions. Then, construct a model based on XGBoost algorithm and input the selected parameters and data into the model for training. Finally, the validity of the proposed method is verified by the data collected from the Project of Chai Jiagou Coal Mine in Tongchuan. The results show that the accuracy rate of coal rock type identification is more than 98% even when the training data is very little and the abnormal data under normal working condition is kept, which confirms the effectiveness and strong robustness of the proposed method.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Guoqiang Huang
AU  - Chengjin Qin
AU  - Ruihong Wu
AU  - Jianfeng Tao
AU  - Chengliang Liu
PY  - 2022
DA  - 2022/12/07
TI  - Intelligent Identification of Coal-Rock Type Based on Boring Parameters of Dig Windlass and XGBoost
BT  - Proceedings of the International Conference of Fluid Power and Mechatronic Control Engineering (ICFPMCE 2022)
PB  - Atlantis Press
SP  - 162
EP  - 178
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-022-0_15
DO  - 10.2991/978-94-6463-022-0_15
ID  - Huang2022
ER  -