In large coal preparation plants with a capacity of 30 million tons/year, the belt speed can reach 7 m/s and the thickness of the material layer can reach 500 mm. Therefore, in high-throughput and complex environments, the problem exists that harmful feeding materials such as iron and gangue are not easily detected, and thus fault diagnosis in the crushers lags behind. Therefore, it is necessary to extract the equipment operation signals from the noisy production environment and identify the feeding materials. Currently, there is no systematic research on signal processing and image classification of crusher feeding materials, while the convolutional neural network (CNN) is outstanding in computer vision. In this paper, sound and vibration signals of the feeding materials are denoised by spectral subtraction and transformed into feature images by continuous wavelet transforms. Then, an image classification model based on CNN is built for these feature images to study its classification mechanism and performance. The results show that the model classification accuracy is respectively 84.0%, 93.5% and 80.1% in coal–iron–wood classification, coal–iron classification, and coal–wood classification. The good classification performance for coal, iron and wood can satisfy the practical demands to remove the harmful feeding materials, which provides the core technical support for the establishment of operating status monitoring and fault diagnosis system of crushing equipment.

The reference value of the oil film thickness and friction coefficient of the slipper pair is critical to the development of the piston pump, especially for 750 mL/r displacement piston pumps. To explore the computing method and range of the reference value mentioned applicable to 750 mL/r displacement piston pumps, this study aims to propose the modified calculation model of the oil film thickness based on the real clearance flowrate and obtain the value range of the friction coefficient of the slipper pair. Through the friction test of the slipper pair, the mean deviation ratio of the oil film thickness between the modified value, theoretical value, and the measured value was calculated and compared, respectively. The variation law of the friction under the influence of different speeds and working pressures was analyzed. Finally, the range of the equivalent friction coefficient with the upper and lower limit surfaces was obtained. The results show that the mean deviation ratio between the modified oil film thickness value and the measured value is mainly within 6%, while that of the theoretical method is mainly from 6% to 8%, and the mean of the difference between the two deviation ratios is about 3%, verifying the feasibility of the modified model used for the calculation of the reference value. Meanwhile, the value of the equivalent friction coefficient fluctuates in the range of 0.006–0.018, which is affected more significantly by the working pressure than the speed, suggesting that the working pressure can be given priority as the design basis of the friction coefficient for 750 mL/r displacement piston pumps.

Profound knowledge of the movement characteristics and spatial distribution of the particles under compression during the crushing of rocks and ores is essential to further understanding kinetic energy release law. Various experimental methods such as high-speed camera technology, the coordinate method, and the color tracking method were adopted to improve the understanding of particles’ movement characteristics and spatial distribution in rock comminution. The average horizontal velocities of the four size particles α, β, γ, and δ are statistically calculated. The descending order of the particles’ average velocity is γ, β, α, and δ. In comparison, the descending order of the particles’ kinetic energy is α, β, γ, and δ. Moreover, the contribution of α particles to the total kinetic energy exceeds 70%. The spatial distribution characteristics of coarse and fine particles show different results. The probability of fine particles appearing in the range closer to the center area is greater, while the position of large particles appears to be more random. The color tracking results show that super-large particles generated by crushing are on the specimen’s surface, while small particles are generally produced from inside. The above results indicate a connection between the particle generation mechanism, movement characteristics, and spatial distribution in the comminution process.

In mineral engineering, harmful foreign objects mixed in the feed can cause faults in the sizer crusher (Sizer). The traditional feed identification methods are inefficient and manual, which fail to adapt to the present production conditions of high belt speed, thick material layer and large throughput. In previous studies, Sizer feed identification focused on single-component rather than mixed feeds. Therefore, we proposed a new Sizer mixed-feed identification method based on continuous wavelet transform (CWT) and squeeze-and-excitation residual network (SE-ResNet). Firstly, the three-channel sensor signals acquired during the Sizer feeding process were subjected to CWT, and the resulting scalograms were fused as the image dataset. Secondly, a Sizer feed identification model based on SE-ResNet was designed for single-component and mixed-feed classification. Ablation studies and method comparisons were conducted to validate the performance of the proposed model. Finally, the method of this paper was tested with industrial datasets to demonstrate its effectiveness and generalizability. The results show that the proposed method can achieve an accuracy of 85.99±0.16% for Sizer mixed-feed identification. This method also significantly improves the identification ability of wood, which solves the problem of distinguishing between coal and wood in the feed. Besides, industrial tests suggest that our method has good Sizer mixed-feed identification performance with an average accuracy of 95.64±1.77%, which can meet the demand of industrial production for the safe and smooth operation of the Sizer.