RT info:eu-repo/semantics/article
T1 Hybrid algorithmic approach oriented to incipient rotor fault diagnosis on induction motors
A1 Martin Diaz, Ignacio
A1 Moríñigo Sotelo, Daniel
A1 Duque Pérez, Óscar
A1 Romero-Troncoso, Rene J.
A1 Osornio Ríos, Roque A.
K1 Induction motor
K1 Fault diagnosis
K1 Artificial intelligence
K1 Simulated annealing algorithm
K1 Oblique random forests
AB This paper investigates the current monitoring for effective fault diagnosis in induction motor (IM) by using random forest (RF) algorithms. A rotor bar breakage of IM does not derive in a catastrophic fault but its timely detection can avoid catastrophic consequences in the stator or prevent malfunctioning of those applications in which this sort of fault is the primary concern. Current-based fault signatures depend enormously on the IM power source and in the load connected to the motor. Hence, homogeneous sets of current signals were acquired through multiple experiments at particular loading torques and IM feedings from an experimental test bench in which incipient rotor severities were considered. Understanding the importance of each fault signature in relation to its diagnosis performance is an interesting matter. To this end, we propose a hybrid approach based on Simulated Annealing algorithm to conduct a global search over the computed feature set for feature selection purposes, which reduce the computational requirements of the diagnosis tool. Then, a novel Oblique RF classifier is used to build multivariate trees, which explicitly learn optimal split directions at internal nodes through penalized Ridge regression. This algorithm has been compared with other state-of-the-art classifiers through careful evaluation of performance measures not encountered in this field.
SN 0019-0578
YR 2018
FD 2018
LK https://uvadoc.uva.es/handle/10324/64932
UL https://uvadoc.uva.es/handle/10324/64932
LA spa
NO ISA Transactions, September 2018, 80, 427-438,
DS UVaDOC
RD 06-ago-2024