In the face of the increasing complexity of risk factors in the coal mining transportation system (CMTS) during the process of intelligent transformation, this study proposes a method for analyzing accidents in CMTS based on fault tree analysis (FTA) combined with Bayesian networks (BN) and preliminary hazard analysis (PHA). Firstly, the fault tree model of CMTS was transformed into a risk Bayesian network, and the inference results of the fault tree and Bayesian network were integrated to identify the key risk factors in the transportation system. Subsequently, based on the preliminary hazard analysis of these key risk factors, corresponding rectification measures and a risk control system construction plan are proposed. Finally, a case study was carried out on the X coal mine as a pilot mine to verify the feasibility of the method. The application of this method effectively identifies and evaluates potential risk factors in CMTS, providing a scientific basis for accident prevention. This research holds significant importance for the safety management and decision making of coal mine enterprises during the process of intelligent transformation and is expected to provide strong support for enhancing the safety and reliability of CMTS.

To quickly repair the inner-surface damage to the hydraulic support cylinder caused by frequent scratches, corrosion, and wear in the process of fully mechanized coal mining, this paper proposes a method of high-frequency induction cladding (HIC) copper alloy on inner-wall of cylinder (IWC) to improve the corrosion, sealing and pressure retention performance of hydraulic cylinders combined with numerical simulation and experimental study. Firstly, a numerical temperature field model for HIC of copper alloy on the IWC is established to investigate various distribution patterns and influencing factors including frequency of induction heating, gap between coil and cladding, power supply rating, cladding thickness and side length of square section of induction coil, etc. Subsequently, an HIC test experiment is conducted to rigorously validate the numerical temperature field model and the experiment employs a meticulously collected dataset of temperature measurements, confirming the model’s accuracy and consistent alignment with anticipated changing trends. In addition, the experiment results were verified through microstructure observation, microhardness testing, friction-wear testing, and electrochemical corrosion parameters, which shows that the factors of induction heating frequency and others have obvious effects on the temperature field distribution of HIC copper alloy on the IWC. Under these working conditions (cladding thickness 1.5 mm, power supply rating 120 kW, heating frequency 120 kHz, gap between the cylindrical workpiece and the induction coil 3 mm, induction coil cross-sectional side length of 10 mm), the thermal impact on the cylinder barrel matrix is minimal, the metallurgical bonding between the cladding layer and the matrix is good, and there are no over burning and porosity defects.

Fully mechanized mining equipment is core to the coal mining process. The selection process for this type of equipment is complex and heavily relies on experts’ experience for determining equipment parameters. This paper proposes a fully mechanized mining equipment parameter prediction model based on Extreme Gradient Boosting Regression Trees (XGBoost), which is developed based on the mapping relationships among geological parameters, fully mechanized mining face conditions, and the parameters of fully mechanized mining equipment. Feature selection is performed based on the feature importance ranking obtained through the Random Forest (RF) method, thereby reducing the model complexity. Different optimization algorithms are used to optimize the hyperparameters of XGBoost, and the results show that the Whale Optimization Algorithm (WOA) outperforms other algorithms in terms of convergence speed and optimization effectiveness. By comparing different prediction algorithms, it is found that the WOA-XGBoost model achieves higher prediction accuracy on the test set, with an average absolute error of 0.0458, root mean square error of 0.1610, and a coefficient of determination (R²) of 0.9451. Finally, a RF-WOA-XGBoost-based parameter prediction model for fully mechanized mining equipment is established, which is suitable for lightly inclined mining faces. This model reduces input complexity, improves the selection speed, minimizes reliance on experts, and ensures prediction accuracy, providing an effective reference for the parameter selection of fully mechanized mining equipment.

To address stability and safety issues in hydraulic support design under inclined–declined and large-dip-angle working conditions, this paper proposes a design-driven dynamic pose–load co-evolution solution method based on the physical entity of the ZFY12000/21/36D hydraulic support. The feasibility of the proposed method is demonstrated through theoretical analysis, spatial modeling, and experimental verification. First, a spatial coordinate system describing hydraulic support pose is established based on Denavit–Hartenberg (DH) theory, constructing a “physical space-geometric coordinate system-DH parameter space” pose mapping model via DH principles, matrix iteration, and kinematic simulation. Second, a load-bearing characteristic analytical method is developed through systematic coupling analysis of dip angle, pose, and load distribution. Finally, coal mine field data collection and hydraulic support test platform experiments analyze load-bearing characteristics under varying poses and loads. Results show Root Mean Square Error (RMSE) values of 0.836° for the front link inclination, 0.756° for the rear link, 0.114° for the balance ram, and 0.372° for the column; load-bearing state evolution under pose–load synergy aligns with theoretical models, confirming method feasibility. This approach fills a domain gap in hydraulic support dip–pose–load co-solving and provides critical references for designing hydraulic support products under extreme dip-angle operations.

To address the low loading rate of shearer drums in thin coal seams under restricted coal-flow space, this study proposes a rotation direction-driven multi-parameter optimization framework based on discrete element method (DEM) simulation. A DEM coal wall model is established to characterize the interaction and transport behavior of coal particles during the cutting process, and a systematic parametric analysis considering drum rotation direction, helix angle, cutting depth, rotational speed, and traction speed is conducted through coupled simulations. The results indicate that rotation direction, helix angle, and traction speed significantly affect coal loading performance, whereas cutting depth and rotational speed have a relatively minor influence. Based on these findings, an optimal parameter combination is identified, where inward rotation with a 20° helix angle and a traction speed of 7 m/min achieves a loading rate of 73.6%. Field application results demonstrate that the proposed method improves coal throwing performance, reduces coal accumulation beneath the ranging arm, and enhances coal flow stability, providing a practical optimization approach for shearer drum performance in thin coal seams and supporting efficient and intelligent coal mining.

This paper addresses the issue of coordination failures in fully mechanized mining equipment under complex operating conditions, which can lead to operational abnormalities and safety hazards. We systematically analyze the dynamic coordination relationships within the equipment system across three dimensions: temporal, spatial, and geometric. Centered on the coal flow, we establish a comprehensive “mining–transportation” coordination mathematical model covering the entire production process from the coal flow cut off by the shearer to the coal flow transported out by the conveyor. Building upon this foundation, a deep learning prediction method integrating long short-term memory (LSTM) and transformer architectures is proposed to construct an intelligent prediction model for the shearer traction speed. This model effectively captures temporal features and long-term dependencies within equipment operation data, enabling the prediction of critical operational parameters for fully mechanized mining systems. It significantly enhances the early identification and warning capabilities for equipment coordination failure states. The experimental results based on the operational data of fully mechanized mining systems show that the LSTM–Transformer model performs excellently in the prediction of traction speed. The mean square error (MSE) of prediction reached 0.041, the mean absolute error (MAE) was 0.122, and the coefficient of determination (R²) was 0.996, fully demonstrating the advantages of the model in terms of prediction accuracy and stability. This article provides a theoretical basis and technical support for the judgment of the operating status of coal mine working faces and the early warning of accident risks, which is of great significance for promoting the intelligent construction of coal mines.