Fault detection and isolation of induction motors using recurrent neural networks and dynamic bayesian modeling

IEEE Transactions on Control Systems Technology

Volumn 18, Issue 2, 2010, Pages 430-437

  Cho, Hyun Cheol ^a   Knowles, Jeremy ^b   Fadali, M Sami ^b   Lee, Kwon Soon ^c  

^a Ulsan College   (South Korea)

^b Reno   (United States)

^c Dong A University   (South Korea)

Author keywords

Dynamic Bayesian model; Fault detection isolation; Induction machines; Recurrent neural networks; Stochastic approximation

Indexed keywords

BAYESIAN MODEL; BAYESIAN MODELING; FAULT CLASSIFICATION; FAULT CONDITIONS; FAULT DETECTION AND ISOLATION; INDUCTION MACHINES; INDUSTRIAL PROCESSS; NEURAL MODELS; NEURAL NETWORK TRAINING; NEW APPROACHES; SIMULTANEOUS PERTURBATION STOCHASTIC APPROXIMATION; STOCHASTIC APPROXIMATION; STOCHASTIC APPROXIMATIONS; SYSTEM BEHAVIORS; SYSTEM OUTPUT; THREE PHASE INDUCTION MOTOR;

APPROXIMATION THEORY; BAYESIAN NETWORKS; FAULT DETECTION; INDUCTION MOTORS; INFERENCE ENGINES; KNOWLEDGE BASED SYSTEMS; MOTORS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

RECURRENT NEURAL NETWORKS;

EID: 77649191505     PISSN: 10636536     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCST.2009.2020863     Document Type: Article

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