摘要
针对传统采煤机记忆截割策略需要频繁人工调整摇臂高度导致效率和精度低等不足,提出利用灰色马尔科夫组合模型的采煤机自适应记忆截割策略。当采煤机截割岩石时,首先根据截割高度先验数据,利用灰色模型得到截割高度预测数据,在此基础上,对该预测数据进行残差计算和状态划分,确定马尔科夫链状态概率矩阵。通过马尔科夫链状态概率矩阵对灰色模型进行反馈修正,得到采煤机截割高度自适应调整值。通过模拟采煤机工作面调整高度,对2种采煤机记忆截割策略进行仿真分析。研究结果表明:传统记忆截割模型可信度为96.26%,但需要5次人工调整,而灰色马尔科夫记忆截割模型的可信度在无人干预下高达99.20%;基于灰色马尔科夫组合模型的采煤机记忆截割策略不仅具有更高的控制精度,而且大大提高了采煤机的自动化水平。
In order to overcome the defects of traditional shearer memory cutting strategy in adjusting rocker arm height frequently and manually,shearer self-adaptive cutting strategy which was based on grey-Markovian model was proposed.With shearer cutting rock,firstly the shearer drum height prediction data using prior data was obtained by the grey model.Based on this,through residual error analysis and state division,the state transferring probability matrix was determined.Then shearer drum height adjusting data was obtained by feed back adjusting from the state transferring probability matrix to the grey model.Finally,the traditional shearer memory cutting strategy was compared with the newly-built strategy by simulating the shearer drum height data from the working face.The results show that the traditional shearer memory cutting strategy has a model reliability of 96.26% with manual adjustment for 5 times;while grey-markovian coupling model reliability is 99.20% without manual adjusting.Shearer drum height adjusting based on grey-markovian coupling model has higher controlling accuracy and is more adaptive for shearer automation.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第10期3054-3058,共5页
Journal of Central South University:Science and Technology
基金
国家高技术研究发展计划("863计划")项目(2008AA062202)
中国矿业大学科技攀登计划项目(2008)
江苏省研究生培养创新工程项目(CXZZ11_0286)
