一种设备实时监控新方法的研究与应用

Research & application of novel real-time equipment monitoring method

根据加权K-means算法,建立历史常规参数的聚类模型,用以实时检测设备运行的参数点。对属于簇团内的参数点,分析簇团内不同相似度时的参数个数比例和阈值,提出紧密(离核)指数的定义;对簇团外的异常点,结合CBLOF(t)的概念,提出了一种新的离群指数的定义,并根据实际情况进行综合,得出了综合指数的定义。据此,可有效地对设备的运行状况进行监控,以起到设备运行优化和故障预警的作用。

To monitor process industry＇s production, the clustering models of history conventional parameters were constructed based on feature weight＇s K-means algorithm. The model was used to conduct real-time inspection in parameters during equipment operation. After analyzing proportions and thresholds among different similarity factors and the whole cluster by different similarity methods, a new factor of compress/scatter was defined. Based on the conception of CBLOF（t）, a new factor of outlier was brought forward to study the realtime outlier. The two factors were combined to a new factor of combination by different instances. Based on the models and the new factors, equipment operations could be effectively monitored so as to optimize equipments process and prevent defects.