By Topic

One-Class SVM based Unusual Condition Monitoring for Risk Management of Hydroelectric Power Plants

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Takashi Onoda ; System Engineering Lab., Central Research Institute of Electric Power Industry, 2-11-1, Iwado Kita Komae-shi, Tokyo 201-8511 JAPAN. phone: +81 3-3480-2111; fax: +81 3-5497-0318; email: ; Norihiko Ito ; Yamasaki Hironobu

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. However, it is very rare to occur abnormal condition and trouble condition in the power equipment. And in order to collect the abnormal and trouble condition data, it is hard to construct experimental power generation plant and hydroelectric power plant. Because its cost is very high. In this situation, we have to find abnormal condition sign as a risk management. 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. Therefore, the proposed method is one method of risk management for hydroelectric power plants.

Published in:

2007 International Joint Conference on Neural Networks

Date of Conference:

12-17 Aug. 2007