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

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.