Winding function approach to simulate induction motors under sleeve bearing fault

Ojaghi, Mansour; Yazdandoost, Nasser

doi:10.1109/icit.2014.6894931

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…Defects associated with the presence of rotor and bearing eccentricity are introduced in the same way: by taking into account the periodic variation of the air gap. Since all inductances in (1) are inversely proportional to the air gap and Carter coefficient, their influence can be determined according to [26]- [29]. A visualization of the defect introduction algorithm is given in Figure 1.…”

Section: Description Of the Defects To Be Implementedmentioning

confidence: 99%

Neural network based detecting induction motor defects supplied by unbalanced grid

Dobroskok

Skakun

Belskii

et al. 2023

IJPEDS

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<span lang="EN-US">Predictive diagnosis of motor defects can reduce repair and downtime costs of electrical equipment. This paper presents the results of a research on the effectiveness of the defect classification and recognition system based on convolutional neural network to detect defects in a squirrel-cage induction motor based on the stator supply voltage and output phase currents when supplied from an industrial grid of limited power with possible voltage asymmetry and harmonic distortion taken into account. In this work a simulation model, implemented in MATLAB/Simulink, is proposed to investigate unbalanced conditions. The mathematical model has been verified on a physical test bench and then used to create a database of currents from measured asymmetric grid voltages in the presence of defects such as short circuit between turns of one phase, rotor bar breakage, rotor eccentricity and bearing defects. The classification quality of all defects in the neural network was 72%, with the exclusion of intercoil short circuit defect and the merging of different bearing defects, the quality of the model was achieved 91%. A further increase in the quality of the defect recognition system is due to a building up modification of the neural network architecture.</span>

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Section: Description Of the Defects To Be Implementedmentioning

confidence: 99%