One-class SVM based unusual condition monitoring for risk management of hydroelectric power plants

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn , Issue , 2007, Pages 857-862

  Onoda, Takashi ^a   Ito, Norihiko ^a   Hironobu, Yamasaki ^b  

^a CENTRAL RESEARCH INSTITUTE OF ELECTRIC POWER INDUSTRY   (Japan)

^b KYUSHU ELECTRIC POWER CO INC   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BEARINGS (STRUCTURAL); COMPUTER NETWORKS; CONDITION MONITORING; HYDROELECTRIC POWER; HYDROELECTRIC POWER PLANTS; LATTICE VIBRATIONS; NEURAL NETWORKS; RISK ANALYSIS; RISK MANAGEMENT; SENSORS; SUPPORT VECTOR MACHINES; VIBRATION ANALYSIS; VIBRATIONS (MECHANICAL);

BEARING VIBRATION; JOINT CONFERENCE; SENSOR DATA;

POWER PLANTS;

EID: 51749114042     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2007.4371070     Document Type: Conference Paper

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