Horseshoe kiln energy consumption anomaly detection method based on improved density peak clustering

Abstract

The invention relates to the technical field of horseshoe kiln energy consumption anomaly detection, in particular to a horseshoe kiln energy consumption anomaly detection method based on improved density peak clustering. An artificial bee colony algorithm is adopted to achieve an adaptive optimization process of a truncation distance, an outlier coefficient strategy is set to achieve a function of automatically dividing cluster centers and outliers, and intelligent, rapid and accurate kiln energy consumption anomaly detection is achieved. According to the method, the adaptive density peak clustering algorithm is modified, and clustering analysis is performed on the energy consumption data in the production process of the glass melting furnace by using the adaptive density peak clusteringalgorithm, so that abnormal energy consumption samples can be efficiently and accurately identified.

Description

technical field [0001] The invention relates to the technical field of abnormal detection of horseshoe kiln energy consumption, and more specifically, to a method for detecting abnormal energy consumption of horseshoe kilns based on improved density peak clustering. Background technique [0002] The horseshoe kiln is a multi-variable complex industrial system, and the variables involved include: sampling time; fuel flow and temperature; combustion air flow and temperature; flame space temperature; liquid surface temperature; temperature etc. In a multi-variable and associated complex parameter system, although the kiln anomalies can be detected by manual inspection of units, manual monitoring log system, or statistical methods (box diagram, Chebyshev theorem, etc.), due to the data acquisition of sensor instruments Data disturbances will affect manual detection due to factors such as abnormalities; moreover, redundant error reports are usually reported when abnormalities oc...

AI Technical Summary

Problems solved by technology

Although the current research on energy consumption anomaly detection has achieved certain results, there is still little research on energy consumption anomaly detection in glass melting furnaces.

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