Neural Computation
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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 order to maintain the safety of hydroelectric power plants, it is very important to measure and collect the sensor data of abnormal condition and trouble condition. However, it is very hard to measure and collect them. Because it is very rare to occur abnormal condition and trouble condition in the hydroelectric power equipment. In this situation, we have to find abnormal condition sign as a risk management from the many sensor data of normal condition. In this paper, we consider that the abnormal condition sign may be unusual condition. This paper shows results of unusual condition patterns detection of bearing vibration. The unusual condition patterns are 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. Therefore, the proposed method is one method of risk management for hydroelectric power plants.