Abstract
SUMMARYThe inspection of power supply facilities can now 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 maintenance quality at demand facilities. The proposed method was developed using maintenance data from demand facilities, collected using time‐based maintenance, and we conduct the analysis primarily using failure data. We use data mining to analyze transaction data that we modeled on the basis of the maintenance data and to construct a “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 identify the objects requiring maintenance which may most likely lead to failures in the future, and this study can contribute to improvement of maintenance technologies for demand facilities using the proposed CBM.
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