2021
DOI: 10.3390/sym13071291
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Feature Ranking and Differential Evolution for Feature Selection in Brushless DC Motor Fault Diagnosis

Abstract: A fault diagnosis system with the ability to recognize many different faults obviously has a certain complexity. Therefore, improving the performance of similar systems has attracted much research interest. This article proposes a system of feature ranking and differential evolution for feature selection in BLDC fault diagnosis. First, this study used the Hilbert–Huang transform (HHT) to extract the features of four different types of brushless DC motor Hall signal. Second, we used feature selection based on a… Show more

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Cited by 12 publications
(15 citation statements)
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References 44 publications
(42 reference statements)
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“…The identification model for the ARX system can be determined using the overall information and parameter vectors given in (10)- (12), respectively:…”
Section: Mathematical Model Of Arx Systemsmentioning
confidence: 99%
See 1 more Smart Citation
“…The identification model for the ARX system can be determined using the overall information and parameter vectors given in (10)- (12), respectively:…”
Section: Mathematical Model Of Arx Systemsmentioning
confidence: 99%
“…One may classify optimization heuristics into four categories: Group one includes methods inspired by human behavior such as balanced teaching-learningbased optimization [8], harmony searches [9] and socio evolution and teaching-learning optimization [10]. Group two includes evolutionary algorithms involving mutation and crossover operations; a few methods in this area include genetic algorithms [11], differential evolution [12], biogeography-based optimizers [13] and bat algorithms [14,15]. Group three includes physics-based techniques involving physical laws for optimization problem solutions; a few techniques in this area are Henry gas solubility optimization [16,17], the big bang-big crunch [18,19] and gravitational search algorithms [20,21].…”
Section: Introductionmentioning
confidence: 99%
“…Differential evolution (DE) is a well-known bio-inspired, population-based, and approximated optimizer that is shown to be effective when solving complex optimization problems, especially those involved in real-world applications as observed in the recently specialized literature [56][57][58]. This method is bio-inspired in the process of natural evolution and was proposed by Storn and Price in 1997 [59].…”
Section: Differential Evolutionmentioning
confidence: 99%
“…The second solution eliminated the mechanical commutator, which, due to its durability, limited the life of the electromachines [ 10 , 11 , 12 ]. The mechanical commutator could cause sparking, which introduced additional electromagnetic disturbances.…”
Section: Introductionmentioning
confidence: 99%