基于决策层的提升机减速器故障诊断方法研究  

Fault Diagnosis Approaches of Mine Hoist Reducer Based on Decision Level

作　　者：王绍进[1] 王健[1] 杨兆建[1] 任芳[1] 

机构地区：[1]太原理工大学机械工程学院,太原030024

出　　处：《煤矿机械》2013年第5期282-284,共3页Coal Mine Machinery

基　　金：山西省留学回国人员基金资助项目(2004-19)

摘　　要：针对提升机减速器提出了一种决策层融合结构模型应用在齿轮故障诊断中。对各个传感器的信息进行神经网络属性判决,再将各神经网络的属性判决进行决策层融合,此种方法具有较强的灵活性,当传感器数目增加时,只需增加神经网络个数就可。对以上信息融合故障诊断方法经过试验研究,证明了方法应用在提升机减速器诊断系统中,减小了错判和漏判,提高了系统的可靠性,保证了提升机安全运行。A decision level fusion structure model proposed for hoist reducer is applied to gear fault diagnosis. Neural network is used for property judgment of information of each sensor. Fuse attribute judgments of each neural network to get a decision fusion. This approach has strong flexibility. When the number of sensors increases, it only need to increase number of neural network. Through experimental study, proves that the application of above information fusion fault diagnosis method in diagnostic system of hoist reduce miscarriage of justice and missing and improve reliability of system, ensuring safe operation of hoist.

关 键 词：提升机 减速器 神经网络 决策层 

分 类 号：TD534[矿业工程—矿山机电]