基于采煤机摇臂销轴自适应加权融合煤岩识别方法研究

Research on Coal Rock Identification Method Based on Adaptive Weighted Fusion of Rocker Arm Pin Shaft of Shearer

针对目前智能截割控制最大阻碍无法判别采煤机的截割状态,不能确定截割的是煤或者岩石问题,提出了基于采煤机摇臂销轴载荷差异的煤岩识别方法。分析采煤机滚筒截割受力,建立滚筒载荷与摇臂销轴载荷之间的力学模型,结合时域自适应数据融合处理RLS算法,使销轴载荷数据方差降低,减小数据波动,使截割煤、岩2种工况下销轴的受力差异更明显,根据销轴受力范围判别此时的截割状态。搭建了采煤机销轴载荷无线传感测试实验台,开展不同煤、岩硬度截割实验。结果表明:工况下采煤机摇臂销轴x,y方向受力均有一定差异,但截岩与截煤工况下载荷范围存在重叠,而采用RLS自适应数据融合算法对载荷数据进行处理,均方差降低了43.8%~87.4%,载荷不存在重叠部分,处理后的载荷差异更加明显,验证了自适应加权融合算法能够更准确达到煤岩判别的目的。

The biggest obstacle of intelligent cutting control at present is that the cutting state of Shearer can not be judged, and the cutting problem is coal or rock, a method of coal rock identification based on load difference of rocker arm pin of shearer is proposed. Analyzes the cutting force of Shearer drum, establishes the mechanical model between drum load and rocker arm pin load, and combines time-domain adaptive data fusion to deal with RLS algorithm, which can reduce the variance of pin load data and reduce the fluctuation of data, the stress difference of the pin shaft is more obvious under the two working conditions of cutting coal and rock, and the cutting state is judged according to the stress range of the pin shaft. A test-bed of Shearer pin load wireless sensing is set up, and different hardness cutting experiments of coal and rock are carried out. The results show that there are some differences in the x and y direction of the rocker pin of the shearer under working conditions, but there is overlap in the load range between rock cutting and coal cutting, the RLS adaptive data fusion algorithm is used to process the load data, the mean square deviation is reduced by 43.8%-87.4%,there is no overlapping part of the load, and the difference of the processed load is more obvious, it is verified that the adaptive weighted fusion algorithm can achieve the goal of coal and rock discrimination more accurately.