2011
DOI: 10.1109/tim.2010.2050980
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A Novel Transform Demodulation Algorithm for Motor Incipient Fault Detection

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Cited by 38 publications
(8 citation statements)
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“…Mostly the examinations were performed on frequency domain in such studies [12][13][14][15]. Some studies used the fuzzy logic [16,17] and ANNs to determine the presence of failure [18][19][20][21][22][23][24]. Also, artificial neural networks were used for determining the presence and type of failures in the internal mechanism of motor [25].…”
Section: Introductionmentioning
confidence: 99%
“…Mostly the examinations were performed on frequency domain in such studies [12][13][14][15]. Some studies used the fuzzy logic [16,17] and ANNs to determine the presence of failure [18][19][20][21][22][23][24]. Also, artificial neural networks were used for determining the presence and type of failures in the internal mechanism of motor [25].…”
Section: Introductionmentioning
confidence: 99%
“…These contributions allow to understand the effect of some phenomena, but do not provide the exact sub-harmonics amplitude introduced by the fault. The faults effects on the stator currents can be grouped into three major contributions, which are: the impact on the stator current power spectral density (PSD) [6], the amplitude and/or phase modulation of the stator currents [7], [8], and the impact over the negative-sequence component [9].…”
Section: Introductionmentioning
confidence: 99%
“…Current monitoring is more attractive for incipient stator turn-fault diagnosis, by which broken bars and air-gap eccentricity can also be detected [11][12][13][14][15]. Therefore, various motor current signature analysis (MCSA) methods have been proposed in order to detect internal faults of IMs [8][9][10][11][12][13][14][15][16][17].…”
Section: Introductionmentioning
confidence: 99%