@inbook{ecdf25c206d74021b61c176c1584f6ef,
title = "Oil whirl fault detection in induction motors using orbital analysis and neural networks",
abstract = "Fault detection in induction motors is a useful practice when some critical processes depend on good machines performance. This work proposes a new computational model for detecting oil whirl faults in induction motors using orbital patterns. Signal vibrations are measured and pre-processed in order to obtain a characteristic orbit that represents the motor condition where an oil whirl fault is present. Through an artificial neural network, the orbital patterns are classified according to the motor condition: good or faulty. Experimental results show a good performance for the proposed model, providing a new tool for recognizing problems in induction motors.",
keywords = "Fault, Induction motor, Oil whirl, Pattern processing",
author = "Hern{\'a}ndez, {Jos{\'e} Juan Carbajal} and Cordero, {Gabriel Longoria} and Fern{\'a}ndez, {Luis Pastor S{\'a}nchez}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2018.",
year = "2018",
doi = "10.1007/978-3-319-56994-9_20",
language = "Ingl{\'e}s",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer",
pages = "286--296",
booktitle = "Lecture Notes in Networks and Systems",
}