基于劣化度的煤矿设备健康状态预测评价研究

Research on Health Status Prediction and Evaluation of Coal Mine Equipment Based on Deterioration Degree

霍宝干河煤矿机电设备功能复杂,设备健康运行状态会受到机器层级等的影响,整机的健康状态由各部件的健康状态来决定,针对整机设备健康状态预测量化难,同时受到主观影响的原因,导致霍宝干河煤矿井下机电设备无法预测未来运行健康状态的问题,选取采煤机为代表进行重点研究,通过传感器进行采煤机数据采集,汇总采集数据极限值,确定采煤机健康状态等级。根据设备状态参数变化,定义劣化度以及明确计算方式;根据层次分析法确定设备关键指标参数的权重;基于机电设备易发故障的层级结构,构建基于劣化度的设备健康状态评价模型,通过数据挖掘预测模型获取预测数据,经过模型的计算,最终获得采煤机健康状态评价向量,后对评价模型进行验证分析。结果分析可得,该预测评价模型可以预测未来4 h的采煤机健康状态并实现预警功能,为煤矿设备的运行安全提供保障。

The mechanical and electrical equipment in Huobao Ganhe Coal Mine has complex functions,and the healthy operation status of the equipment is affected by machine hierarchy and other factors.The health status of the entire machine is determined by the health status of each component.In view of the reason that it is difficult to predict and quantify the equipment health status of the entire machine and affected by the subjective impact at the same time,which leads to the problem of inability to predict the future operating health status of the downhole mechanical and electrical equipment in Huobao Ganhe Coal Mine,the coal mining machine is selected as a representative for key research.Data collection of the coal mining machine is carried out through sensors,and the limit values of the collected data are summarized to determine the health status level of the coal mining machine.Define deterioration degree and clarify calculation method according to changes in equipment status parameters;Determine the weight of key equipment indicator parameters according to the Analytic Hierarchy Process;Based on the hierarchical structure of mechanical and electrical equipment prone to faults,an equipment health status evaluation model based on deterioration degree is constructed.Predictive data is obtained through data excavating prediction models,and after model calculation,the health status evaluation vector of the coal mining machine is finally obtained.The evaluation model is then verified and analyzed.The result analysis shows that the predictive evaluation model can predict the health status of the coal mining machine in the next 4 h and achieve early warning function,providing assurance for the safe operation of coal mining equipments.