Considering that the trajectory of the cutting head of the longitudinal axis roadheader directly affects the surface morphology of the roadway, the formation mechanism of the outer profile of the roadway obtained by the cutting head is analyzed, and the dynamic model of the cutting part was constructed. The variation law of the vibration swing angle of the cutting head under different working conditions was obtained, and the coordinate of the cutting head of the roadheader in the roadway space coordinate system was determined by using the robot kinematics analysis method. The mathematical model of cutting the outer outline of the roadway by the cutting head is constructed. Through the numerical calculation and analysis, the characteristics of the formed outer profile of the roadway were analyzed considering the influence of the vibration of the cutting head. The results show that the difference between the “pit” and the “peak ridge” obtained by considering the vibration of the cutting head is larger than that without considering the vibration of the cutting head, which shows that the outer outline of the roadway obtained by the real cutting is more rougher than that without considering the vibration of the cutting head. With the increase of the cutting lifting angle, the maximum and average value of the difference between the “pit” and the uplift “peak ridge” on the coal and rock shows an increasing trend, and with the increase of the cylinder pressure, the maximum values of the horizontal swing angle “pit” and the uplift “peak ridge” gradually increase. The vibration of the cutting head when cutting coal and rock will have a great influence on the outer profile of the roadway, which cannot be ignored in the research of accurate autonomous cutting control algorithm of roadheader. The research results of this paper can provide a basis for the optimization of cutting parameters and high-precision forming control method of roadheader.
In recent years, the monitoring range of source location technology has developed from being one-dimensional and two-dimensional to being three-dimensional. However, due to the complexity and nonuniformity of the seismic wave propagation medium and the uncertainty of the propagation law, there will be large errors in the source location results. Therefore, the analysis of vibration signal has become the key problem of current research. This paper designs a microseismic monitoring system based on Internet of Things sensors, which can monitor the vibration wave characteristics of vibration signals. In order to test the positioning accuracy of the system, this paper introduces three positioning methods: target positioning method based on time difference, time delay estimation method based on EMD, and source target positioning method based on the characteristic frequency of vibration signal. The purpose of this paper is to find the most accurate method from the three source location methods. Through these three methods, the vibration source generated by a single person walking in situ can be located in the vibration positioning experiment of human walking. The error between the actual position and the measurement source position is compared. The results show that the time delay estimation method based on empirical mode decomposition has the highest positioning accuracy. In addition, in the microseismic experiment, it is proved that the positioning accuracy of EMD using L1 norm statistical criterion is higher than that using L2 norm statistical criterion.
The mining of open pit mines is widespread in China, and there are many cases of landslide accidents. Therefore, the problem of slope stability is highlighted. The stability of the slope is a factor that directly affects the mining efficiency and the safety of the entire mining process. According to the statistics, there is a 15 percent chance of finding landslide risk in China’s large-scale mines. And due to the expansion of the mining scale of the enterprise, the problem of slope stability has become increasingly obvious, which has become a major subject in the study of open-pit mine engineering. In order to better predict the slope stability coefficient, this study takes a mine in China as a case to deeply discuss the accuracy of different algorithms in the stability calculation, and then uses a deep learning algorithm to study the stability under rainfall conditions. The change of the coefficient and the change of the stability coefficient before and after the slope treatment are experimentally studied with the displacement of the monitoring point. The result shows that the safety coefficient calculated by the algorithm in this paper is about 7% lower than that of the traditional algorithm. In the slope stability analysis before treatment, the safety factor calculated by the algorithm in this paper is 1.086, and the algorithm in this paper is closer to reality. In the stability analysis of the slope after treatment, the safety factor calculated by the algorithm in this paper is 1.227, and the stability factor meets the requirements of the specification. It also shows that the deep learning algorithm effectively improves the efficiency of the slope stability factor prediction and improves security during project development.
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