Deep learning for fault diagnosis based on multi-sourced heterogeneous data

POWERCON 2014 - 2014 International Conference on Power System Technology: Towards Green, Efficient and Smart Power System, Proceedings

Volumn , Issue , 2014, Pages 740-745

  Ma, Yan ^a   Guo, Zhihong ^a   Su, Jianjun ^a   Chen, Yufeng ^a   Du, Xiuming ^a   Yang, Yi ^a   Li, Chengqi ^a   Lin, Ying ^a   Geng, Yujie ^a  

^a STATE GRID SHANDONG ELECTRIC POWER RESEARCH INSTITUTE   (China)

Author keywords

classification; deep learning; fault diagnosis; restricted Boltzmann machine

Indexed keywords

ABSTRACTING; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; ELECTRIC CIRCUIT BREAKERS; FAILURE ANALYSIS; FAULT DETECTION; IMAGE ANALYSIS; LEARNING SYSTEMS; POWER TRANSFORMERS; UNSUPERVISED LEARNING;

BOLTZMANN MACHINES; COMPREHENSIVE ANALYSIS; DEEP BOLTZMANN MACHINES; FEATURE LEARNING; HETEROGENEOUS DATA; LINEAR CLASSIFIERS; RESTRICTED BOLTZMANN MACHINE; SUPERVISED TRAININGS;

DEEP LEARNING;

EID: 84925271494     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/POWERCON.2014.6993854     Document Type: Conference Paper

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