Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

Blodt, M.; Granjon, Pierre; Raison, Bertrand; Rostaing, Gilles

doi:10.1109/tie.2008.917108

Cited by 479 publications

(229 citation statements)

References 29 publications

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“…The IA does not change significantly, which means that the bearing fault introduces a frequency modulation, which can be highlighted by the variance of the instantaneous frequency. This result is in accordance with the theoretical study and the experimental validation in [15].…”

Section: Iv3 Summary On Demodulation Techniquessupporting

confidence: 92%

“…It has been demonstrated in [15], [60] that single-point bearings faults induce mechanical eccentricities, but also load-torque variations. Hence, in the carried-out simulations, bearing faults are emulated by generating only one sort of physical phenomena: rotating eccentricities at bearing characteristic fault frequency !…”

Section: Iii1 Induction Machine Modeling Under Faults Brieflymentioning

confidence: 99%

“…In fact, in [14][15], the authors have presented an analytical approach to model the impact of the mechanical and bearing faults. This approach is based on traditional MMF and permeance wave approach for the airgap magnetic flux density computation [16].…”

Section: Introductionmentioning

confidence: 99%

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Condition Monitoring of Induction Motors Based on Stator Currents Demodulation

Elbouchikhi

Choqueuse²,

Benbouzid³

2015

IREE

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Section: Iv3 Summary On Demodulation Techniquessupporting

confidence: 92%

Section: Iii1 Induction Machine Modeling Under Faults Brieflymentioning

confidence: 99%

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Condition Monitoring of Induction Motors Based on Stator Currents Demodulation

Elbouchikhi

Choqueuse²,

Benbouzid³

2015

IREE

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show abstract

“…). These specific faultindicators and their unique signatures in terms of frequency are extensively discussed in [10] and can be summarized for outer race, inner race, ball and bearing cage faults, respectively as [4], [8]- [10], [12], [21]- [26]:…”

Section: Methodsmentioning

confidence: 99%

Single point outer race bearing fault severity estimation using stator current measurements

Corne

Vervisch

Derammelaere

et al. 2017

2017 IEEE International Electric Machines and Drives Conference (IEMDC)

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Abstract-Although numerous valuable research has been conducted by many authors, the relation between evolving mechanical faults and their fault-indicating reflections in the electrical signals of rotating electric machinery still requires extensive attention. This lack of information implies serious obstructions in the evolution of applying motor current signature analysis as a complete and reliable condition monitoring technology. However, as previous research of the authors resulted in the construction of a unique test-bench where specific mechanical faults can be introduced into an 11kW induction machine with high reproducibility and accuracy, the relations between evolving mechanical faults and their reflections in the stator current can be investigated and quantified experimentally. Using this mechanical fault-emulator, experiments where conducted where the severity of a single point outer race bearing fault and its corresponding signature in the stator current's spectral fault-components are accurately analyzed. These evolving faulty components are trended and characterized, resulting in the desired quantification of the mechanical fault-severity. Based on these results, it is possible to inversely estimate the severity of single point outer race bearing problems by acquiring nothing more then the stator current timefunctions.

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“…Thus, the characteristic stator current frequencies f c in terms of characteristic vibration frequencies f v will be calculated by [19]:…”

Section: Fault Detection Of Im Bearingsmentioning

confidence: 99%

Health Condition Monitoring of Induction Motors

Wang¹,

Li²

2015

Induction Motors - Applications, Control and Fault Diagnostics

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Induction motors (IMs) are commonly used in various industrial applications. A spectrum synch (SS) technique is proposed in this chapter for early IM defect detection using electric current signals; fault detection in this work will focus on defects in rolling element bearings and rotor bars, which together account for more than half of IM imperfections. In bearing fault detection, the proposed SS technique will highlight the peakedness of the fault frequency components distributed over several fault related local bands. These bands are synchronized to form a fault information spectrum to accentuate fault features. A central kurtosis indicator is proposed to extract representative features from the fault information spectrum and formulate a fault index for incipient IM fault diagnosis. The effectiveness of the developed SS technique is tested on IMs with broken rotor bars and with damaged bearings.

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Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

Cited by 479 publications

References 29 publications

Condition Monitoring of Induction Motors Based on Stator Currents Demodulation

Condition Monitoring of Induction Motors Based on Stator Currents Demodulation

Single point outer race bearing fault severity estimation using stator current measurements

Health Condition Monitoring of Induction Motors

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