Drill tools sticking prediction based on adaptive long short-term memory

Abstract

As one of the most severe disasters in deep coal mining, rockburst can be prevented through drill-hole pressure relief. However, the coal mine is characterized by high crustal stress and changeable mechanical properties of surrounding rock, which will cause drill rod deflection phenomenon, then lead to rod-deflection sticking accidents. This paper proposes a prediction method based on adaptive long short-term memory (ALSTM) for rod-deflection sticking accidents to improve drilling efficiency and reduce sticking accidents. Firstly, the sticking data is obtained through the intelligent drilling condition simulation experimental platform, and then the sticking features are extracted based on the sticking data. Secondly, the sticking factor is constructed, and the sticking critical line is set. Thirdly, the good-point set and the proposed random perturbation algorithm are employed to improve the spotted hyena optimizer (SHO) to obtain the improved spotted hyena optimizer (ISHO). Finally, we use the ISHO to optimize the hyperparameters of the long short-term memory and then establish the sticking prediction model based on ALSTM. The experimental results show that the proposed prediction model meets the demands for sticking prediction very well.