摘要
为实现采煤机记忆截割过程中截割轨迹的实时动态调节,采用理论分析与实验的方法,分析了采煤机滚筒截割过程中的受力情况,应用最小模糊度方法建立滚筒x、y、z轴受力的隶属度函数,根据采集到的截割力信号判定截齿的实时截割环境,采用模糊神经网络控制方法对传统的记忆截割系统进行优化,并通过实验验证系统的精确性与可靠性.研究结果表明:改进后的系统能优化采煤机的记忆截割路径,大大降低截齿的磨损,延长了滚筒的整体使用寿命,提高了综采工作面的生产效率.
In order to realize the real-time dynamic adjustment of cutting trajectory in the memory cutting process of shearer, this paper adopted the methods of theory analysis and experiment, and analyzed the force of the shearer drum cutting process. The membership function of the minimum fuzzy degree method is established to realize x, y, z axis cylinder force. According to the real-time determination of cutting force of cutting tooth cut signal collected by cutting environment, the traditional memory cutting system is optimized by using the fuzzy neural network control method, and the accuracy and reliability of the system are verified by experiments. The results of the study show that the improved system can optimized the cutting path through the self-learning function of the shearer memory cutting optimization neural network based on the cutting force signal of the cutting pick, greatly reduce the tooth wear and extends the service lifespan of the whole drum, and improve the production efficiency of fully mechanized working face.
出处
《辽宁工程技术大学学报（自然科学版）》
CAS
北大核心
2016年第6期642-645,共4页
Journal of Liaoning Technical University (Natural Science)
基金
辽宁省教育厅科学技术研究(创新团队)项目(LT2010045)
