RT info:eu-repo/semantics/article
T1 An Experimental Comparative Evaluation of Machine Learning Techniques for Motor Fault Diagnosis Under Various Operating Conditions
A1 Martín Díaz, Ignacio
A1 Moríñigo Sotelo, Daniel
A1 Duque Pérez, Óscar
A1 Romero-Troncoso, Rene J.
AB The diagnosis of electric machines, such as induc-tion motors, is one of the key tasks that needs to be performed to guarantee their right operation as electromechanical energy con-verters in most industrial facilities. The ability to reliably identify a mechanical fault occurrence before it becomes catastrophic can reduce risks related to the productive chain. Recently, different in-telligent approaches have been proposed to develop feature-based methods for automatic rotor fault diagnosis of induction motors. This paper provides an experimental comparative evaluation of different machine learning techniques for rotor fault identifica-tions. The classifiers are tested with data obtained under different operating conditions of the ones used to train them, as it is usual in industry. The input information is obtained from current signals of an induction motor with two states of rotor bar degradation under two preestablished load levels.
PB IEEE
SN 0093-9994
YR 2018
FD 2018-02
LK https://uvadoc.uva.es/handle/10324/64938
UL https://uvadoc.uva.es/handle/10324/64938
LA spa
NO IEEE Transactions on Industry Applications, May/Jun 2018, 54, 3, 2215-2224,
DS UVaDOC
RD 18-nov-2024