On accurate and reliable anomaly detection for gas turbine combustors: A deep learning approach

Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM

Volumn , Issue , 2015, Pages 440-447

  Yan, Weizhong ^a   Yu, Lijie ^b  

^a GE GLOBAL RESEARCH   (United States)

^b General Electric Power and Water   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHEMICAL DETECTION; DEEP LEARNING; FAULT DETECTION; FEATURE EXTRACTION; GAS TURBINES; GASES; HEALTH; LEARNING ALGORITHMS; LEARNING SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS; SPEECH RECOGNITION; SYSTEMS ENGINEERING;

BREAKTHROUGH TECHNOLOGY; DETECTION PERFORMANCE; EXHAUST GAS TEMPERATURES; GAS TURBINE COMBUSTOR; GAS-TURBINE COMBUSTION; MACHINE LEARNING TECHNOLOGY; NATURAL LANGUAGE PROCESSING; NEURAL NETWORK CLASSIFIER;

COMBUSTORS;

EID: 85016121433     PISSN: 23250178     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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