In view of the nonlinear and nonstationary characteristics of the weak fault signal of rolling bearings and the characteristics that are easy to be masked by strong background noise, a weak fault diagnosis method of rolling bearings combining Variational Mode Decomposition (VMD) and Maximum Correlated Kurtosis Deconvolution (MCKD) is proposed. In order to realize the adaptive parameter selection of VMD and MCKD, the particle swarm optimization algorithm was used to optimize the parameters in the two algorithms. Firstly, the Particle Swarm Optimization (PSO) was used to optimize the α and K in the VMD algorithm, and then the optimal mode components were selected based on the results of VMD decomposition of weak fault signals. Secondly, the PSO is used to optimize the sum in the MCKD algorithm, and then the fault shock component in the optimal component signal is strengthened based on the MCKD algorithm. Finally, the weak fault characteristics of the bearing were extracted by the envelope spectrum. The experimental results show that this method can adaptively enhance the impact component in the weak fault of the bearing, and effectively extract the weak fault characteristics of the bearing submerged by the strong noise.

As key coal mining equipment, the shearer is crucial for improving adaptability to complex conditions, extending service life, and reducing failure rates—key to enhancing coal production economic benefits. Using the transmission system model, dynamic bearing contact forces, vibration acceleration at virtual measurement points, and vibration levels in gangue/pure coal conditions are analyzed. The reliability and accuracy of the finite element analysis for the cutting unit's vibration characteristics were validated via bench test comparison. The vibration-data-equipped housing is input into acoustic boundary element simulation; combined with the built model, noise variations at different sound field response points with gear face width and bottom clearance coefficient are derived. Results show high-order modal analysis confirms no system resonance risk. When the motor gear face width is 74mm, planetary gear train face width 140mm, and bottom clearance coefficient 0.4, the gears exhibit excellent load-bearing capacity, stability, and low vibration/noise.

The loading rate of coal is significantly influenced by the number of vanes on shearer drums. However, in actual production, 1400 mm diameter drums feature two-vane and three-vane designs, while 2240 mm diameter ones have three-vane and four-vane designs, with the vane number corresponding to the optimal coal-loading rate remaining unclear. To reveal the correlation between vane number and coal-loading rate for such drums, parameters were calibrated through multiple physical tests in this study. Supported by field data, simulation analyses were conducted via the discrete element method to investigate the effect of the vane number on the drum coal-loading rate under different moisture contents and traction speeds. The results indicated that particle adhesion initially increases and then decreases with the moisture content, with the peak characteristics influenced by the particle size. Particle movement during drum coal mining is jointly governed by multiple factors. For 1400 mm drums, two or three vanes should be selected depending on moisture fluctuations and coal transportation requirements, whereas for 2240 mm drums, three or four vanes are recommended based on the balance between coal-cutting volume, conveying capacity, and traction speed.

Underground bauxite comprehensive mechanized mining has attracted growing attention, yet regional differences in bauxite characteristics challenge its applicability and economic efficiency. The conical pick, a key cutting tool for this mining method, has cutting load characteristics that directly impact the cutting mechanism’s efficiency and reliability. Three typical bauxite samples from different mining areas were selected as research objects. After testing their composition and firmness coefficients, a linear cutting test bench was used to measure their tri-axial cutting forces at various cutting depths. Regression analysis and the linear fitting of cutting coefficients were conducted to study cutting force and normal force (two core cutting loads). The results show that power functions effectively describe the relationships between the cutting force, normal force, fluctuation coefficient and cutting depth. As the bauxite firmness coefficient and cutting depth rise, the cutting force peak value, fluctuation amplitude and frequency all increase. Excessively high or low cutting coefficients reduce the cutting efficiency; only when both the cutting coefficient and the depth are in an optimal range can the cutting efficiency reach its maximum.