Accident Prediction in Coal Mine Drilling Using Fuzzy Inference Based on Multi-Scale Feature Extraction

To ensure the safety of coal mine drilling operation and reduce losses caused by accidents, this study proposes a fuzzy-reasoning-based early prediction method for in-hole accidents during underground coal mine drilling processes. First, the mechanism of in-hole accidents during underground drilling in coal mines was analyzed, and the changes in different accident-related parameters were summarized. Second, based on the suddenness of different accidents, they were distinguished into sudden-change and slow-change accidents, and the corresponding features were extracted from short- and long-timescale information, respectively. Subsequently, a rule base was constructed based on the analysis of field data and manual experience, and different accident occurrence probabilities were determined using fuzzy reasoning to realize the early prediction of in-hole accidents in the coal mine drilling process. Finally, WinCC was used to design the upper computer interface for in-hole accident prediction in underground drilling processes in coal mines. It displays the results of three types of in-hole accident prediction: drill bit failure, stuck pipe, and bit drop.