Mechanical Engineering College,Liaoning Technical University,Fuxin　 123000,China

提高采煤自动化、无人化程度的关键在于提高采煤机对煤炭和岩石的识别能力,在记忆截割的基础上,采用灰色预测理论,提出了1种基于滚筒采煤机摇臂惰轮轴受力分析的综合煤岩识别方法,通过实时检测采煤机在截割不同介质时的惰轮轴受力,并根据惰轮轴受力建立采煤机截割路线智能预测系统,实时修正截割路线,提高了采煤机的追踪适应能力。该方法在中煤张家口煤机厂实验平台上进行截割实验验证,结果表明:采煤机割岩时受力比割煤时平均受力大19.45%,能够很好的对煤岩界面进行识别。

The key of improve the degree of coal mining automation and unmanned mining is to improve the coal and rock identification ability of shearer. On the basis of memory cutting,this study proposed a coal and rock identification method using grey prediction theory based on the force of idler shaft in shearer’ s ranging arm. The idler shaft stress was monitored in real-time when the shearer cut different cutting media,and the intelligent prediction system of shear- er’s cutting route was established according to the idler shaft stress,the cutting route was corrected in real-time. As a result,the tracking and adapt ability of the shearer was improved. The method was tested on the experiment platform in Zhangjiakou coal mine machinery factory. The result shows that the average stress of shearer with rock cutting is larger than coal cutting by 19. 45% . Thus,shearer is able to distinguish the interface between coal and rock.

Tian Liyong,Mao Jun,Wang Qiming. Coal and rock identification method based on the force of idler shaft in shearer’ s ranging arm[ J]. Journal of China Coal Society,2016,41(3):782-787.