Detection of induction motor faults - Combining signal-based and model-based techniques

Proceedings of the American Control Conference

Volumn 6, Issue , 2002, Pages 4531-4536

  Parlos, Alexander G ^a   Kim, Kyusung ^a   Bharadwaj, Raj ^a  

^a Department of Electrical and Computer Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FAULT DIAGNOSIS SYSTEMS; MULTIRESOLUTION SIGNAL PROCESSING METHODS;

INDUCTION MOTORS; MATRIX ALGEBRA; RECURRENT NEURAL NETWORKS; SIGNAL PROCESSING; VECTORS;

ELECTRIC FAULT LOCATION;

EID: 0036058019     PISSN: 07431619     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACC.2002.1025365     Document Type: Conference Paper

References (6)

1. A new approach to on-line turn fault detection in AC motors
2. AI techniques in induction machines diagnosis including the speed ripple effect
3. An adaptive statistical time-frequency method for detection of broken bars and bearing faults in motors using stator current
4. New results on recurrent network training: Unifying the algorithms and accelerating convergence
5. Multi-step-ahead prediction in complex systems using dynamic recurrent neural networks