Analytic Model for Induction Motors Under Localized Bearing Faults

Ojaghi, Mansour; Sabouri, Mahdi; Faiz, Jawad

doi:10.1109/tec.2017.2758382

Cited by 39 publications

(22 citation statements)

References 25 publications

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“…As a common fault, the bearing fault distribution varies between 40% -90% from large to small machines [33]. Some possible causes, such as electric corrosion, lubricant impurities, misalignment in assembly and the overall vibration, may lead to cracks, pits, and spalls on the rolling surface [34]. Among all the above root causes of bearing faults, the shaft voltages and the associated bearing currents are the most common causes of accelerated bearing degradation [5].…”

Section: A Electrical Corrosion Of the Bearingmentioning

confidence: 99%

Bearing Corrosion Failure Diagnosis of Doubly Fed Induction Generator in Wind Turbines Based on Stator Current Analysis

Chen

Islam

2020

IEEE Trans. Ind. Electron.

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Section: A Electrical Corrosion Of the Bearingmentioning

confidence: 99%

Bearing Corrosion Failure Diagnosis of Doubly Fed Induction Generator in Wind Turbines Based on Stator Current Analysis

Chen

Islam

2020

IEEE Trans. Ind. Electron.

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“…Therefore, some authors have proposed alternatives such as the use of advanced analytical models [45,46] or combined FEM-analytical model [47] to reduce the computation time of FEM models. They are based on the equivalent circuit parameters calculation of IM by FEM models [48,49].…”

Section: Faulty Induction Motor Modelsmentioning

confidence: 99%

Induction machine model with finite element accuracy for condition monitoring running in real time using hardware in the loop system

Sapena-Bañó

Chinesta

Pineda-Sánchez

et al. 2019

International Journal of Electrical Power & Energy Systems

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Most industrial processes are run by induction machines (IMs). Condition monitoring of IM assures their continuity of service, and it may avoid highly costly breakdowns. Among the methods for condition monitoring, online motor current signature analysis is being attracting a rising interest, because it is non-invasive, and it can identify a wide variety of faults at early stage. To favour the development of on-line fault diagnosis techniques, it is necessary to have real-time currents with which test the new techniques and devices. Models running in real time in hardware-in-the-loop (HIL) simulators are a suitable alternative to balance the drawbacks of test benches (costly, limited machines, faults and working conditions). These models must be accurate enough to reflect the effects of a fault and they must be running in real time. A promising technique based on the equivalent circuit parameters calculation of IM by finite element analysis (FEA) is attracting a rising interest due to its reliability, performance and the possibility of being run in a HIL. Nevertheless, prior to running in a HIL, it is necessary to compute the IM parameters using FEA, which requires long simulation times and high computing resources. Consequently, covering a whole range of degrees of a giving fault could be unaffordable. What is proposed in this paper is to apply the sparse subspace learning (SSL) in combination with the hierarchical Lagrangian interpolation (HLI) to obtain the parametric solutions of the faulty IM model that cover the whole range of severity of a given fault, with a reduced number of FEA simulations. By means of this approach it is possible not only to boost the computation speed but also to achieve a significant reduction of memory requirements while retaining reasonable accuracy compared to traditional FEA, so enabling the real-time simulation of predictive models.

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“…ϕ = 0 for an outer race defect ω r t for an inner race defect (15) where ω r is an angular frequency of the rotor.…”

Section: Air-gap Functionmentioning

confidence: 99%

Spectral analysis for diagnosis of bearing defects in induction machine drives

Jafarian

Nazarzadeh

2019

IET Electric Power Applications

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In this study, the finite element method (FEM) is utilised for precise analysis and detection of the bearing faults in induction machine drives. For this purpose, the machine inductances in the presence of slots, teeth, and various bearing failures are obtained by FEM. In addition, the spectrum of the stator currents are analysed by employing the electrical model of the induction machine drives. Some experimental and numerical results are illustrated that the proposed model can consider small bearing failures in high-noise environments and thus can be used for analysing and detecting bearing failures in induction machine drives.

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Analytic Model for Induction Motors Under Localized Bearing Faults

Cited by 39 publications

References 25 publications

Bearing Corrosion Failure Diagnosis of Doubly Fed Induction Generator in Wind Turbines Based on Stator Current Analysis

Bearing Corrosion Failure Diagnosis of Doubly Fed Induction Generator in Wind Turbines Based on Stator Current Analysis

Induction machine model with finite element accuracy for condition monitoring running in real time using hardware in the loop system

Spectral analysis for diagnosis of bearing defects in induction machine drives

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