Dynamic and gradual coupled reliability analysis of the transmission system of a shearer cutting arm

The strong impact and heavy load imposed on the transmission system of a shearer cutting arm is a potential cause of system variation and deterioration. And the overall equipment performance and output efficiency of a drum shearer cutting arm depends to a large degree on the reliability of the transmission system. Therefore, the current study proposes an innovative method for precise prediction of the reliability of the transmission system considering dynamic characteristic and performance degradation. Firstly, for the sake of quantifying the time-dependent reliability with deterioration parameters, a dynamic model of the geared rotor system is established based on the finite element method and the dynamic response is derived based on the main failure mode and the stochastic process model. Then the reliability and reliability sensitivity are estimated based on the probability perturbation theory and the fourth-order moment method. Finally, the proposed method is validated through a comparison to the canonical Monte Carlo Simulation method, which exhibits an average discrepancy of merely 3%. The present method serves as a theoretical guidance and applicable tool for both design and manufacture of coal shearers.

Design and Application of Simulating Cutting Experiment System for Drum Shearer

When the shearer cuts coal or rock with different hardness, it will produce corresponding cutting state information. This paper develops a simulation cutting experiment system for the drum shearer based on similarity theory. It took the spiral cutting drum of a shearer as the research target and derived the principal similarity coefficients through the dimensional analysis method. Meanwhile, this paper designed the structure of the cutting power system and hydraulic system. Then, it chose a certain amount of coal powder as an aggregate, cement 325# as cementing material, sand, and water as auxiliary materials to prepare simulated coal samples. The paper adopted the orthogonal experiment method and used a proportion of cement, sand, and water as the influencing factors in designing a simulated coal sample preparation plan. In addition, it utilized the range analysis method to research the influence of various factors on the density and compressive strength of simulated coal samples. Finally, it conducted simulated coal sample cutting tests. The results show that the density of the simulated coal samples is between 1192.59 Kg/m3–1483.51 Kg/m3, and the compressive strength range reaches 0.16 MPa–3.94 MPa. The density of the simulated coal sample is related to the mass proportion of cement and sand. When the ratio gradually increases, the influence of sand increases. Furthermore, the compressive strength is linearly proportional to the proportion of cement. The self-designed simulation cutting experiment system could effectively carry out the relevant experiments and obtain the corresponding cutting condition signals through the sensors. There are differences in vibration signals generated by cutting different strength materials. Extracting the kurtosis value as the characteristic value can distinguish various cutting modes, which can provide a reliable experimental solution for the research of coal-rock identification.

A Novel Approach for Acoustic Signal Processing of a Drum Shearer Based on Improved Variational Mode Decomposition and Cluster Analysis

During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic signal processing of a shearer based on the parameter optimized variational mode decomposition (VMD) method and a clustering algorithm. First, the particle swarm optimization (PSO) algorithm searched for the best parameter combination of the VMD. According to the results, the approach determined the number of modes and penalty parameters for VMD. Then the improved VMD algorithm decomposed the acoustic signal. It selected the ideal component through the minimum envelope entropy. The PSO was designed to optimize the clustering analysis, and the minimum envelope entropy of the acoustic signal was regarded as the feature for classification. We then use a shearer simulation platform to collect the acoustic signal and use the approach proposed in this paper to process and classify the signal. The experimental results show that the approach proposed can effectively extract the features of the acoustic signal of the shearer. The recognition accuracy of the acoustic signal was high, which has practical application value.

Comparison of the Efficiency of Various Flow Charts of Longwall Coal Mining

For increasing the efficiency of longwall coal mining a mathematical model and a simulation model of the longwall coal mining were developed. 2D and 3D visualizations of the execution of the simulation model were realized. The main goal of the modeling of coal mining in a fully-mechanized stoping face is the comparison of flow charts of Longwall coal mining, the evaluation of performance of a drum shearer depending on such factors as technical parameters of the drum shearer, size of a stoping face, flow charts of the drum shearer operation, changing geomechanical characteristics of the coal seam. As a result of simulation the dependences of the drum shearer performance and annual mine profits on the length of the stoping face and flow charts of the drum shearer operation such as one-way flow chart, shuttle flow chart and bench flow chart were obtained.

A DATA DRIVEN DECISION MAKING APPROACH FOR LONG-WALL MINING PRODUCTION ENHANCEMENT

Machine failures have destructive effects on continuity of operation and lead to production losses in long-wall mines, making proper maintenance scheduling essential. This paper models the reliability of the whole production chain in an Iranian long-wall mine including the drum shearer, Armored Face Conveyor (AFC), hydraulic powered supports, Beam Stage Loader (BSL), and main conveyer belt. Analyzing the computational results and failure frequencies, we rank the critical components and develop a reliability-based preventive maintenance schedule for all equipment. In respect to the data classification, conveyor belt with failure abundance of 41.5 percent is the most critical, while powered supports with the failure abundance of 1.2 percent shows the best performance. Approximately, the reliability of the production process after four hours reaches nearly to zero. Implementing the schedule, computational results suggest an increase of approximately 67.7 percent in the average production per shift.