Unusual Condition Mining for Risk Management of Hydroelectric Power Plants

Abstract

Kyushu Electric Power Co.,Inc. collects different sensor data and weather information to maintain the safety of hydroelectric power plants while the plants are running. In this paper, we consider that the abnormal condition sign may be unusual condition. This paper shows results of unusual condition patterns of bearing vibration detected from the collected different sensor data and weather information by using one class support vector machine. The result shows that our approach may be useful for unusual condition patterns detection in bearing vibration and maintaining hydroelectric power plants