基于云模型理论的矿井风机故障率预测分析  

Prediction and Analysis of the Failure Rate of Mine Fan based on Cloud Model Theory

作　　者：戴剑勇[1] 王彬 邓先红 DAI Jianyong;WANG Bin;DENG Xianhong(School of Resource Environment and Safety Engineering,University of South China,Hengyang 421000,China)

机构地区：[1]南华大学环境与安全工程学院,湖南衡阳421000

出　　处：《矿业安全与环保》2019年第2期88-91,97,共5页Mining Safety & Environmental Protection

基　　金：国家自然科学基金项目(51174116)

摘　　要：为了解决井下风机故障率较高、故障发生趋势难以有效预测的问题,采用云模型不确定性推理方法对矿井风机故障率预测问题进行研究。通过挖掘风机老化状况及外部影响因素2个评价参数与风机故障发展趋势之间的关系,构建了基于云发生器的风机老化指数—外部影响因素评价的双因素云推测模型,并阐明了风机故障云推测模型的实施步骤。实例应用结果表明,该预测模型具有较高的预测精度,能够有效解决井下风机故障率预测过程中的随机性、不确定性等问题。In order to solve the problem that the failure rate of downhole fan is high and the failure trend is difficult to predict effectively,the uncertainty reasoning method of cloud model is adopted to study the prediction of failure rate of mine fan.By exploring the relationship between the fan aging status and the two evaluation parameters of external influencing factors and the development trend of fan failure,a two-factor cloud prediction model of the fan aging index-external influencing factors evaluation based on the cloud generator was constructed,and the implementation steps of the cloud prediction model of fan failure were illustrated. The application results show that the model has high prediction accuracy and can effectively solve the problems of randomness and uncertainty in the prediction of failure rate of downhole fan.

关 键 词：矿井风机 云模型 故障率 老化指数 外部影响因素 

分 类 号：TD441[矿业工程—矿山机电] TH43[机械工程—机械制造及自动化]