Real-time fault diagnosis for gas turbine generator systems using extreme learning machine

Neurocomputing

Volumn 128, Issue , 2014, Pages 249-257

  Wong, Pak Kin ^a   Yang, Zhixin ^a   Vong, Chi Man ^a   Zhong, Jianhua ^a  

^a UNIVERSITY OF MACAU   (Macao)

Author keywords

Extreme learning machine; Gas turbine generator system; Kernel principal component analysis; Real time fault diagnosis; Time domain statistical features; Wavelet packet transform

Indexed keywords

DATA HANDLING; FAILURE ANALYSIS; GAS PLANTS; GAS TURBINE POWER PLANTS; GAS TURBINES; KNOWLEDGE ACQUISITION; LEARNING ALGORITHMS; PRINCIPAL COMPONENT ANALYSIS; REAL TIME SYSTEMS; SUPPORT VECTOR MACHINES; TIME DOMAIN ANALYSIS; TURBOGENERATORS; WAVELET ANALYSIS; WAVELET TRANSFORMS;

EXTREME LEARNING MACHINE; KERNEL PRINCIPAL COMPONENT ANALYSES (KPCA); REAL-TIME FAULT DIAGNOSIS; STATISTICAL FEATURES; WAVELET PACKET TRANSFORMS;

FAULT DETECTION;

ACCURACY; ALGORITHM; ARTICLE; CLASSIFICATION; CLASSIFIER; EXTREME LEARNING MACHINE; GAS TURBINE GENERATOR SYSTEM; INFORMATION PROCESSING; KERNEL METHOD; LINEAR SYSTEM; MACHINE LEARNING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NONLINEAR SYSTEM; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROCESS TECHNOLOGY; REAL TIME FAULT DIAGNOSTIC SYSTEM; RELIABILITY; SIMULATION; SUPPORT VECTOR MACHINE;

EID: 84893049069     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.03.059     Document Type: Article

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