Selection of Sensors that Influence Trouble Condition Sign Discovery based on a One-class Support Kernel Machine for Hydroelectric Power Plants

摘要

Trouble conditions rarely occur in the equipment of hydroelectric power plants. Therefore, it is important to find indicator signs for trouble conditions. In a previous study, we proposed a trouble condition sign discovery method, which consists of two detection stages. In the first stage, we can discover trouble condition signs, which are different from the usual condition data. In the second stage, we can monitor aging degradation, with plant experts confirm these trouble condition signs in daily operations. Hence, there is a need to detect these trouble condition signs using a small number of sensors. In this paper, we propose a method for narrowing down the sensors used in trouble condition sign discovery. This paper shows the experimental results of trouble condition sign detection for bearing vibration based on the collected data from different sensors using our proposed method and our previously proposed method. The experimental results show that even if the number of sensors is reduced, our proposed method can find trouble condition signs, which are different from the usual condition data. Therefore, the proposed method may be useful for trouble condition sign discovery in hydroelectric power plants.