Unusual condition monitoring based on support vector machines for 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. It is very rare to occur trouble condition in equipment of hydroelectric power plants. And in order to collect the trouble condition data, it is hard to construct experimental power generation plant and hydroelectric power plant. In this situation, we have to find trouble condition sign. In this paper, we consider that the rise inclination of unusual condition data gives trouble condition sign. This paper shows results of detecting unusual condition data of bearing vibration from the collected different sensor data and weather information by using one class support vector machine and analyzing the trend of generating unusual condition data by using a support vector machine. The result shows that our approach may be useful for unusual condition data detection in bearing vibration and maintaining hydroelectric power plants.