• Journal of the Korean GEO-environmental Society
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• v.24 no.12
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• pp.61-67
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• 2023

Heat pipes located underground in urban areas and operated under high temperature and pressure conditions can cause large-scale human and economic damage if damaged. In order to predict damage in advance, damage and construction information of heat pipe are analyzed to derive independent variables that have a correlation with frequency of damage, and a simple regression analysis modified model using each variable is applied to the field. However, as the correlation between independent variables applied to the model increases, the independence between variables is harmed and the reliability of the model decreases. In this study, the independence of the pipe diameter, burial depth, insulation level of monitoring system, and disconnection or short circuit of the detection line, which are judged to be interrelated, was tested to derive a method for combining variables and setting categories necessary to apply to the frequency of damage estimation model. For the test of independence, the continuous variables pipe diameter and burial depth were each converted into three categories, insulation level of monitoring system was converted into two categories, and the categorical variable disconnection or short circuit of the detection line status was kept as two categories. As a result of the test of independence, p-value between pipe diameter and burial depth, level of monitoring system and disconnection or short circuit of the detection line was lower than the significance level (α = 0.05), indicating a large correlation between them. Therefore, the pipe diameter and burial depth were combined into one variable, and the categories of the combined variable were set to 9 considering the previously set categories. The insulation level of monitoring system and the disconnection or short circuit of the detection line were also combined into one variable. Since the insulation level is unreliable when the detection line status is disconnection or short circuit, the categories of the combined variable were set to 3.

• Journal of the Korean GEO-environmental Society
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• v.24 no.12
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• pp.47-52
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• 2023

In order to efficiently maintain heat pipes operated by district heating operators, the facility history and damage history data built by the operator are used to identify key independent variables that are related to the occurrence of damage. Afterwards, the correlation with the frequency of damage was analyzed, and a basic model for estimating the frequency of damage was derived. Considering the correlation with the estimation model based on the use time currently being used by domestic and foreign district heating operators, a simple regression analysis basic model was presented as the independent variable with the highest correlation between continuous variables such as the use time, pipe diameter, burial depth, and insulation level of monitoring system, and the frequency of damage. The remaining independent variables were reflected as factors that modify and supplement the basic model. As a result of the analysis, as in previous research cases, it was confirmed that the analysis model between use time and frequency of damage had the highest correlation between the two variables and could be used as a basic model. Pipe diameter, burial depth, and insulation level of monitoring system information have also been confirmed to have a correlation with the frequency of damage, so they can be used as factors to supplement the basic model.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.244-251
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• 2014

In this study, we give a detailed account of normal and abnormal state of GIS(Gas Insulated Switch-gear) using the preventive diagnostic technology. And it is based on the analysis and diagnosis for storing data of GIS by intelligent GIS monitoring system. The wave shape of GIS sound is similar to noise and is systematically generated by discharge and its corona sound. Therefore, in this paper, to classify normal and abnormal GIS sound. We could discriminate between normal and abnormal case using level crossing rate(LCR) and spectrogram energy rate.