Radiator working state monitoring method and system based on data analysis

By performing cluster analysis on multi-dimensional parameter data of the radiator, and combining local density and fluctuation, the problem of low monitoring efficiency and accuracy in existing technologies has been solved, realizing intelligent and precise monitoring of the radiator, and improving the operational reliability and maintenance efficiency of the equipment.

CN121834616APending Publication Date: 2026-04-10GUANGZHOU JUNKAI POWER EQUIP CO LTD
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Patent Information

Application Number
CN202610160496.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-04
Publication Date
2026-04-10

AI Technical Summary

Technical Problem

Existing radiator condition monitoring methods mainly rely on manual inspections and simple temperature sensors, which suffer from untimely monitoring, insufficient accuracy, difficulty in achieving intelligent analysis, and failure to comprehensively consider multi-dimensional factors, resulting in low monitoring efficiency and accuracy.

Method used

By collecting multi-dimensional parameter data of the radiator, using density peak clustering and hierarchical clustering algorithms to cluster data points, and combining local density and fluctuation degree calculations, abnormal data points are identified, enabling intelligent and precise monitoring of the radiator.

Benefits of technology

It improves the sensitivity and accuracy of radiator monitoring, enabling timely identification of potential faults, providing accurate early warning capabilities, and enhancing the operational reliability and maintenance efficiency of the equipment.

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Abstract

The invention relates to the technical field of electric digital data processing, in particular to a radiator working state monitoring method and system based on data analysis, and the method comprises the steps: collecting multi-dimensional parameter data of a radiator at different moments, and taking the multi-dimensional parameter data of the radiator at the same moment as a data point; clustering all the data points to obtain a plurality of clusters; the abnormal degree of any cluster is calculated, the abnormal degree is in inverse correlation with the number of the data points in each cluster and is in positive correlation with the standard deviation of the data points in each cluster, the data points in the cluster with the abnormal degree larger than a set threshold value are responded to be abnormal data points, and it is judged that the radiator is abnormal at the moment corresponding to the abnormal data points. The problem that the monitoring efficiency and accuracy are not high in the process of monitoring the abnormality of the radiator is solved.
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