Hybrid algorithmic approach oriented to incipient rotor fault diagnosis on induction motors

被引:20
作者
Martin-Diaz, Ignacio [1 ,2 ]
Morinigo-Sotelo, Daniel [1 ]
Duque-Perez, Oscar [1 ]
Osornio-Rios, Roque A. [3 ]
Romero-Troncoso, Rene J. [3 ]
机构
[1] Univ Valladolid, Escuela Ingn Ind, Elect Engn Dept, Sede Paseo Cauce, Paseo Cauce 59, E-47011 Valladolid, Spain
[2] Univ Guanajuato, DICIS, HSPdigital CA Telemat, Carr Salamanca Valle Km 3-5 1-8, Guanajuato 36885, Mexico
[3] Univ Autonoma Queretaro, Fac Ingn, HSPdigital CA Mecatron, Rio Moctezuma 249, San Juan Del Rio 76806, Queretaro, Mexico
关键词
Induction motor; Fault diagnosis; Artificial intelligence; Simulated annealing algorithm; Oblique random forests; GENETIC ALGORITHM; BROKEN BARS; MUSIC; LOAD;
D O I
10.1016/j.isatra.2018.07.033
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 seventies 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.
引用
收藏
页码:427 / 438
页数:12
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