Electric motor fault detection and diagnosis by kernel density estimation and kullback-leibler divergence based on stator current measurements

IEEE Transactions on Industrial Electronics

Volumn 62, Issue 3, 2015, Pages 1770-1780

  Giantomassi, Andrea ^a   Ferracuti, Francesco ^a   Iarlori, Sabrina ^a   Ippoliti, Gianluca ^a   Longhi, Sauro ^a  

^a UNIVERSITÀ POLITECNICA DELLE MARCHE   (Italy)

Author keywords

AC motors; Current measurement; Electric motors; Fault detection; Fault diagnosis; Induction motors; Monte Carlo methods; Principal component analysis; Probability density function; Rotors

Indexed keywords

AC MOTORS; COST REDUCTION; ELECTRIC CURRENT MEASUREMENT; ELECTRIC FAULT CURRENTS; ELECTRIC MOTORS; FAILURE ANALYSIS; INDUCTION MOTORS; MONTE CARLO METHODS; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY; PROBABILITY DENSITY FUNCTION; PROBABILITY DISTRIBUTIONS; ROTORS; ROTORS (WINDINGS); SOCIAL NETWORKING (ONLINE); STATISTICS; TRACTION MOTORS;

COMPUTATIONAL PERFORMANCE; FAULT DETECTION AND DIAGNOSIS; FAULT IDENTIFICATIONS; KERNEL DENSITY ESTIMATION; KULLBACK LEIBLER DIVERGENCE; MOTOR CURRENT SIGNATURE ANALYSIS; MOTOR FAULT DETECTION AND DIAGNOSIS; THREE-PHASE CURRENTS;

FAULT DETECTION;

EID: 84923172290     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2014.2370936     Document Type: Article

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