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Statistical data fusion as diagnosis scheme applied to a kinematic chain

Author
Arellano, F.; Saucedo, J.; Osornio, R.; Delgado Prieto, M.; Cariño , J.A.; Romero-Troncoso, R.
Type of activity
Presentation of work at congresses
Name of edition
19th International Conference on Industrial Technology
Date of publication
2018
Presentation's date
2018-02-21
Book of congress proceedings
2018 IEEE International Conference on Industrial Technology (ICIT): Lyon, France: February 19-22, 2018: proceedings
First page
2111
Last page
2118
DOI
https://doi.org/10.1109/ICIT.2018.8352515 Open in new window
URL
https://ieeexplore.ieee.org/document/8352515/ Open in new window
Abstract
In the modern industry framework, the application of condition monitoring schemes over electromechanical systems is being subjected to demanding requirements. Industrial electrical machinery implies the consideration of an electric motor, but also, gearboxes, shafts and couplings among others, resulting in complex kinematic chains. Such electromechanical configurations increase the risk of multiple faults coexistence and overlapping of corresponding effects in the considered physical magnitudes....
Keywords
Condition monitoring, Data fusion, Discriminant analysis, Feature estimation, Feature reduction, Induction motor, Induction motors, Kinematics, Linear discriminant analysis, Multiple faults, Neural networks, Pattern recognition, Principal component analysis, Stator currents, Stators, Time domain analysis, Vibration analysis, Vibrations, Vibrations Chains
Group of research
MCIA - Motion Control and Industrial Applications Research Group

Participants

  • Arellano, Francisco  (author and speaker )
  • Saucedo Dorantes, Juan Jose  (author and speaker )
  • Osornio Rios, Roque A.  (author and speaker )
  • Delgado Prieto, Miquel  (author and speaker )
  • Cariño Corrales, Jesús Adolfo  (author and speaker )
  • Romero Troncoso, René de Jesús  (author and speaker )