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Due to the complex geological and mechanical environment of coal-bearing strata, the feed resistance of the drilling rig feed system and the load torque of the rotary system are complex and diverse. The existing technology only controls the actuator through established procedures for procedural actions. The adaptive intelligent control level of the drilling process is low. When the drilling conditions change, it is easy to cause accidents such as sticking and breaking. It reduces the drilling efficiency of the drilling robot and affects the work cycle. To solve this problem, a dual loop PID adaptive control method for feed and rotation of drilling robots based on coal rock drillability is proposed. Firstly, with drilling efficiency and drilling safety as control objectives, drilling pressure and torque are selected as input parameters of the coal rock drillability model. Wavelet packet decomposition is used to extract features of drilling process data to obtain sample data and test set. BP neural network is used for training and verification to establish coal rock drillability model and obtain recommended drilling speed and rotation speed under current drilling conditions. Secondly, based on the coal rock drillability model, a constant torque control strategy and a constant drilling speed control strategy based on PID control are designed. The drilling robot feed rotation control system adjusts the set drilling pressure through a constant torque control circuit to achieve constant torque control. The system adjusts the set torque through a constant drilling speed control circuit to achieve constant drilling speed control, improving drilling efficiency while ensuring its safe operation. Finally, A drill-rock interaction model reflecting the feed swing load is developed. The simulation testing is conducted on the dual loop PID adaptive control method for the feed rotation of the drilling robot. The results show the following points. ① When the hardness of coal and rock remains unchanged, this control method can achieve constant torque and constant drilling speed control, with torque maintained at 2 000 N·m and drilling speed maintained at 6 mm/s. ② At 50 seconds, by increasing the hardness of coal and rock and adopting an adaptive adjustment strategy, the drilling robot can quickly reach a stable state in terms of drilling pressure, rotation speed for the rotary control system. ③ If the recommended drilling speed of 6 mm/s corresponds to an actual torque of 2 350 N·m which exceeds the permissible load torque for the operation of the drilling robot and the actual speed of 85 r/min is less than 95% of the recommended speed, the recommended drilling speed setting is reduced by means of the drilling speed trim module. The drilling pressure is adjusted to adjust the drilling robot's torque to the optimal torque, ensuring that the drilling robot is stable again in the constant torque and constant drilling speed control state.

In view of difficulties in obtaining stratum information parameters and low accuracy of lithology identification in existing lithology identification method of coal-bearing stratum in coal mine underground, an optimized identification method of coal-bearing stratum lithology based on principal component analysis (PCA) algorithm and kernel fuzzy C-means clustering (KFCM) algorithm was proposed. A high-dimensional drilling parameters set was constructed by using drilling test rig to obtain six kinds of drilling sensitive parameters, such as penetration rate, rotary torque, drilling pressure, rotational speed, rotary pressure and mud pump flow rate, which was taken as identification data sources, including training samples and test samples. Combining feature extraction advantage of PCA algorithm and good clustering effect of KFCM algorithm, a lithology identification model based on PCA-KFCM algorithm was established. The PCA algorithm was used to extract features of the training samples and reduce the dimension of the data to obtain eigenvalues and eigenvectors of the training samples. KFCM algorithm was used to conduct fuzzy core clustering on principal component data sets of training samples, and the test rock samples were divided into several types. The criterion was established by the Mahalanobis distance method, and the formation lithology of the test samples was identified by the minimum Mahalanobis distance. The test results show that the optimized identification method of coal-bearing stratum lithology based on PCA-KFCM algorithm can effectively identify formation lithology, and the identification accuracy is improved by 23.2% compared with the conventional KFCM algorithm.

Thanks to the complex underground environment that coal mines enjoy, drill rods for mining are vulnerable to corrosion during operation. To investigate the impact of the corrosion defects on the residual intensity of the drill rod, a relational expression of stress versus corrosion rate in the conditions of uniform and local corrosions is deduced based on the theory on mechanochemical effects; building on this, a spherical corrosion defect is developed in the joint of a φ73 mm drill rod while it is exerted with a make-up torque, an axial force and a bending moment. So it is found that, when the corrosion defect is under pressure, the bending moment plays a certain role to inhibit its increase, however when it is under tension, the bending moment plays the role to drive its increase so as to quicken its corrosion rate while the impact of the change in the corrosion detect depth is much greater than that of the radius. The result from the research provides a basis to evaluate the residual intensity of the drill rod and theoretical basis to protect drill rods from corrosion.

Inertial navigation system (INS) is widely used as inclinometer in coal drilling rig to realize navigation and attitude measurement. Vibration of coal drilling rig is one of the significant issues impacting the performance of alignment. Previous work has indicated that part of alignment error caused by vibration of gyroscope can be suppressed by inertial alignment frame. However, vibration error of accelerometer cannot be well suppressed. In this paper, an inertial alignment based on improved empirical mode decomposition (EMD) filter is proposed to address vibration issue in alignment. Moreover, a prediction strategy of extreme endpoints based on relativity of extreme points is proposed to reduce the endpoint error to suppress the accumulative error in EMD filter. The proposed alignment method is applied in laboratory condition. The result shows that the stability of accelerometer improves 42.43% at most, the alignment time reduce from 300 to 100 s and the alignment error of inclination angle is less than 5.4 arcmin. The experiment in coal mining rig shows the alignment time reduces from 300 to 50 s. The proposed alignment method improves the performance of inclinometer effectively and feasibly in alignment period and the improved EMD filter can be also applied in navigation period to suppress the vibration error of accelerometer. [ABSTRACT FROM AUTHOR]