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With the increasing application of milling technology in near-horizontal directional drilling stuck/buried drilling accidents in coal mines, the probability of second stuck/buried drilling accidents with milling tools increases. To solve this problem, based on the conventional hydraulic mechanical internal cutter, a ratchet type two-stage positioning small diameter internal cutter was designed for the milling tool of near-horizontal directional drilling in underground coal mine; the displacement relationship between the push device and the cutting tool was obtained by establishing the geometric model, and the technical parameters of the ratchet two-stage positioning small diameter internal cutter were determined. In the field application, the casing milling drill pipe with a diameter of 114 mm was cut off successfully at 446.5 m. The results show that the piston and the throttle needle cooperate to cut the signal function; the ratchet mechanism with positioning function is adopted to reduce the dependence on continuous water pressure and reduce the operation difficulty. Using spring energy storage to provide flexible thrust force for cutting tool rotation, the thrust is continuous and stable, and the impact of water pressure fluctuation on tool tip is eliminated.

The basic design principles and structural characteristics of the feeding device of drilling rig in underground coal mine are analyzed. According to the demand of long-stroke, a feeding device by the series and parallel combination cylinder is developed, the theoretical calculation method of key parameters of the feeding device is given, and the changes of effective feeding force and pulling force are analyzed by fitting curve. The analysis shows that the effective feeding force and pulling force decrease with the increase of wear. The maximum feeding force and pulling force of the drilling rig were tested, and the validity of the theoretical calculation method was verified.

In order to realize the coal mine drilling hole sealing automation, according to the “13th Five-Year” key research and development project “gas control drilling and hole sealing robot” requirements, we put forward the integral multi-channel automatic hole sealing device overall scheme, designed the automatic hole sealing control method, and studied and designed the parameters of grouting channel and hole sealing channel. Finite element method was used to analyze the setting dynamics of the hole sealing device, and the drilling setting and grouting simulation tests were carried out on the prototype of the hole sealing device. The research shows that the whole structure of the hole sealing device can be equipped with the drilling robot to grasp, install and switch the channel automatically to seal the hole automatically. It can meet the requirements of sealing holes with different sealing lengths and different inclination angles. The three-way structure can meet the requirements of setting, exhaust, grouting, gas extraction and other technological processes on pressure and pressure loss; according to the dynamic analysis of the contact stress between the setting seal capsule and the hole wall, the axial friction resistance between the setting seal capsule and the hole wall, as well as the simulation experiment results of sealing the hole, when the setting grouting pressure of the device is 6 MPa, the grouting pressure of the sealing hole can reach 3 MPa, which fully meets the technical requirements of “two blocks and one grouting” sealing hole in the coal mine, and the negative pressure loss of the extraction channel is less than 2 kPa. By meansof sensors and instruments, the monitoring of setting pressure, sealing grouting pressure, extraction flow, pressure and other parameters can be realized.

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. [ABSTRACT FROM AUTHOR]

This study establishes a prediction model based on the back propagation (BP) neural network for controlling the underground directional drilling path in a coal mine. The four-layer BP neural network model chooses 11 trajectory parameters (dip angles and azimuths from 12 m after the measurement while drilling (MWD)) and two control parameters as inputs. Two parameters, the dip angle and azimuth at bit, are the outputs. Trained with data from 502 groups, the model was used to forecast 12 test data groups. The results were then compared with the prediction results of artificial experience from 24 technicians. The study shows that the mean absolute error of the dip angle and azimuth at bit are only 0.51° and 0.68°, respectively, as predicted by the prediction model, which uses the logsig activation function and has a double-hidden-layer with a point structure of 9×6. The prediction error also follows a normal distribution. Compared with technicians who have worked for more than five years, the accuracy of prediction results from the BP neural network model is reduced by 33.9% and meets the needs of drilling path control. [ABSTRACT FROM AUTHOR]