工业工程
工業工程
공업공정
Industrial Engineering Journal
2011年
2期
118~121
,共null页
数据挖掘 加权关联规则 神经网络 故障诊断
數據挖掘 加權關聯規則 神經網絡 故障診斷
수거알굴 가권관련규칙 신경망락 고장진단
data mining; weighted association rules; neural network; fault diagnosis
采用加权关联规则算法对设备历史数据库进行挖掘,建立加权关联规则模式库。设备监控数据通过与模式库匹配,实现设备故障诊断。同时,针对钢铁企业中液压设备的特殊性,提出利用自组织竞争神经网络模型确定权值,即将设备故障信息的3个主要属性：重要程度、易损程度、故障等级作为模型的输入,通过训练样本确定设备故障的加权关联规则的权值。实例证明了该方法的有效性。
採用加權關聯規則算法對設備歷史數據庫進行挖掘,建立加權關聯規則模式庫。設備鑑控數據通過與模式庫匹配,實現設備故障診斷。同時,針對鋼鐵企業中液壓設備的特殊性,提齣利用自組織競爭神經網絡模型確定權值,即將設備故障信息的3箇主要屬性：重要程度、易損程度、故障等級作為模型的輸入,通過訓練樣本確定設備故障的加權關聯規則的權值。實例證明瞭該方法的有效性。
채용가권관련규칙산법대설비역사수거고진행알굴,건립가권관련규칙모식고。설비감공수거통과여모식고필배,실현설비고장진단。동시,침대강철기업중액압설비적특수성,제출이용자조직경쟁신경망락모형학정권치,즉장설비고장신식적3개주요속성：중요정도、역손정도、고장등급작위모형적수입,통과훈련양본학정설비고장적가권관련규칙적권치。실예증명료해방법적유효성。
With historical equipment database,weighted association rule algorithm is adopted to conduct data mining.By using weighted association rules,a model base is established.Based on the model base,fault diagnosis can be performed by using equipment monitor data.In the meanwhile,self-organizing competitive neural network model is used to determine the weight of hydraulic equipment in a steel enterprise.Three properties are considered.They are degree of importance,degree of vulnerability,and level of fault.The model takes these three properties of the fault information as inputs and determines the connection weights of equipment fault through sample training.Experiments show that this algorithm can improve the accuracy of fault diagnosis of hydraulic equipment.
