Improved Inspection of Facilities for High-Voltage Class using Datamining

Abstract

In recent years, the inspection of power supply facilities can be conducted with high accuracy using remote monitoring technology. In contrast, it is difficult to install sensors at demand facilities because their scale and installation environment differ among customers. As a result, the demand facilities are inspected at fixed time intervals. In this paper, we propose Condition-Based Maintenance (CBM), which improves the maintenance quality at the demand facilities. The proposed method is developed using the maintenance data of demand facilities, which has been collected using Time-Based Maintenance (TBM) thus far, and we conduct analysis primarily using failure data. We use Datamining to analyze transaction data that we modeled based on the maintenance data and to construct the “failure predictive model” that can predict the failure of facilities and its causes from the results of the analysis. By using the constructed model, we will be able to define an object that requires maintenance, which may most likely lead to failures in the future, and this study can contribute to improvement in maintenance technologies for the demand facilities using the proposed CBM.