Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mining. The least squares support vector machine (LSSVM) has been proven to offer strong potential in prediction and classification issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. In this paper, an improved fly optimization algorithm (IFOA) to optimize the parameters of LSSVM was presented and the LSSVM coupled with IFOA (IFOA-LSSVM) was used to identify the shearer cutting pattern. The vibration acceleration signals of five cutting patterns were collected and the special state features were extracted based on the ensemble empirical mode decomposition (EEMD) and the kernel function. Some examples on the IFOA-LSSVM model were further presented and the results were compared with LSSVM, PSO-LSSVM, GA-LSSVM and FOA-LSSVM models in detail. The comparison results indicate that the proposed approach was feasible, efficient and outperformed the others. Finally, an industrial application example at the coal mining face was demonstrated to specify the effect of the proposed system.

With the aim to improve the performance of magnetorheological fluids (MRFs) for mechanical transmission system, a process to prepare silicone oil-based MRFs with the addition of nanometer Fe₃O₄ particles is presented and five MRFs samples with different mass fraction of nanometer Fe₃O₄ particles have been prepared. The experimental materials, the preparation process, and test methods are elaborated. Moreover, the microstructures of soft magnetic carbonyl iron particles, nanometer Fe₃O₄ particles, and carbonyl iron/nano-sized Fe₃O₄ composites have been characterized via scanning electron microscope (SEM). Finally, test experiments of sedimentation stability, zero field viscosity, and shear yield stress have been carried out. The experimental results show that adding a certain amount of nanometer Fe₃O₄ particles (4 and 6 wt%) into MRFs can improve the performance of MRFs.

As the sound signal of a machine contains abundant information and is easy to measure, acoustic-based monitoring or diagnosis systems exhibit obvious superiority, especially in some extreme conditions. However, the sound directly collected from industrial field is always polluted. In order to eliminate noise components from machinery sound, a wavelet threshold denoising method optimized by an improved fruit fly optimization algorithm (WTD-IFOA) is proposed in this paper. The sound is firstly decomposed by wavelet transform (WT) to obtain coefficients of each level. As the wavelet threshold functions proposed by Donoho were discontinuous, many modified functions with continuous first and second order derivative were presented to realize adaptively denoising. However, the function-based denoising process is time-consuming and it is difficult to find optimal thresholds. To overcome these problems, fruit fly optimization algorithm (FOA) was introduced to the process. Moreover, to avoid falling into local extremes, an improved fly distance range obeying normal distribution was proposed on the basis of original FOA. Then, sound signal of a motor was recorded in a soundproof laboratory, and Gauss white noise was added into the signal. The simulation results illustrated the effectiveness and superiority of the proposed approach by a comprehensive comparison among five typical methods. Finally, an industrial application on a shearer in coal mining working face was performed to demonstrate the practical effect.

Accurate and rapid recognition of coal and gangue is an important prerequisite for safe and efficient mining in top coal caving face. In this paper, a novel coal-gangue recognition method is put forward based on improved ant-lion optimization algorithm (IALO), variational modal decomposition (VMD) and improved MobileNetV2 network. Firstly, two strategies of trap boundary adjustment and chaotic mapping are designed for ALO to sufficiently explore the solution space and prevent falling into local optimization. Subsequently, IALO is employed to search the optimal parameters of VMD and the superiority of IALO-VMD can be reasonably embodied through some simulation analysis. Then, the vibration signal-image mapping is performed to produce rich sample data for MobileNetV2. The coordinate attention mechanism and Inception structure are combined with MobileNetV2 to accelerate the training speed and improve the classification accuracy, and the improved MobileNetV2 based classifier is constructed to fulfill an automatic coal-gangue recognition. Finally, an experimental platform of coal-gangue impacting the tail beam of hydraulic support is built and many comparison experiments are carried out. The experiment results indicate that the proposed coal-gangue recognition method has the prediction accuracy of 99.66%, which is increased by 1.43% compared to the classical MobileNetV2. The underground on-site testing results show that the average prediction accuracy of the proposed coal-gangue recognition model can exceed 93%, and can effectively and accurately distinguish different top coal caving states.