摘要
为了减少人为疏漏对设备管理的影响,确保煤矿设备有效运行,提出基于齐次马尔科夫随机过程的设备状态预测模型。用最小二乘估计方法确定设备状态划分标准,划定设备状态空间,优化设备检修计划。经过与实际统计数据的对比可知,当样本容量足够大时该模型能可靠预测设备状态,为煤矿设备检修计划优化提供了一个有效的解决方案。
An state prediction model for coal mine equipment is proposed based on homogeneous Markov random process, to optimize the maintenance plan ensuring effective run and avoiding personal error. The least square estimation method is used to define the criteria of equipment state space and its separation. The comparison with practical statistical data shows that if the sample amount is large enough, the model predicts reliably the state of equipment and provides a way for optimizing their maintenance plan.
出处
《煤矿机电》
2014年第4期108-111,共4页
Colliery Mechanical & Electrical Technology
关键词
煤矿设备管理
检修计划
马尔科夫随机过程
management of coal mine facilities
maintenance plan
Markov random process
