Study on Active DCS Data Acquisition Technology of High Power Consumption Anomaly in Industrial Heating Electric Furnace
The phenomenon of high power consumption is easy to occur in the operation of industrial heating electric furnace,and the abnor-mal power data is generated with high frequency and large amount of data,which reduces the collection efficiency of abnormal data.Therefore,the active data acquisition technology of DCS(Distributed Control System)for high power consumption anomaly of industrial heating electric furnace is proposed.The local mean value is used to calculate the missing DCS data of industrial heating,the Lagrange interpolation method is used to interpolate it,and the data after interpolation is standardized.The power unbalance feature matrix was constructed,the Local Outlier Factor(LOF)algorithm was used to extract the feature set of power consumption anomaly data,and the quantile regression model based on Long Short-Term Memory network(LSTM)was constructed.To realize the active collection of abnormal DCS data of industrial electric fur-nace.The experimental results show that the proposed method can effectively improve the acquisition efficiency and accuracy,the average ac-quisition time is only 0.29ms,and the accuracy rate-recall rate is over 98％.
industrial heating electric furnaceDCS datadata collectionanomalous featureLSTM regression model
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