Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current

Leite, Valeria Cristina Maria Nascimento; Silva, Jonas Guedes Borges da; Veloso, Giscard Francimeire Cintra; Silva, Luiz Eduardo Borges da; Lambert‐Torres, Germano; Bonaldi, Erik Leandro; Oliveira, Levy de Lacerda de Ely

doi:10.1109/tie.2014.2345330

Cited by 231 publications

(128 citation statements)

References 41 publications

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“…In a rolling-element bearing, artificially produced defect is introduced by simply drilling a hole in the outer raceway to simulate a localized fault, which is similar to other literatures [9][10][11][12].…”

Section: Resultsmentioning

confidence: 99%

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Effects of the Slot Harmonics on the Stator Current in an Induction Motor with Bearing Fault

Song¹,

Hu²,

Zhu³

et al. 2017

Mathematical Problems in Engineering

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Bearing faults are the most frequent faults of induction motors. The current spectrum analysis is an easy and popular method for the monitoring and detection of bearing faults. After a presentation of the existing fault models, this paper illustrates an analytical approach to evaluate the effects of the slot harmonics on the stator current in an induction motor with bearing fault. Simple and clear theoretical analysis results in some new current characteristic frequencies. Experimental tests with artificial bearing outer raceway fault are carried out by measuring stator current signals. The experimental results by spectral analysis of the stator current agree well with the theoretical inference.

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Section: Resultsmentioning

confidence: 99%

“…Therefore, MCSA has received considerable attention due to its low cost and noninvasive nature in recent years [7,[9][10][11][12][13][14]. Moreover, stator current signals are often previously measured for control purposes in the drive system and can be analyzed remotely and online [9,11].…”

Section: Introductionmentioning

confidence: 99%

Effects of the Slot Harmonics on the Stator Current in an Induction Motor with Bearing Fault

Song¹,

Hu²,

Zhu³

et al. 2017

Mathematical Problems in Engineering

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“…A mais simples delas é a que examina a amplitude do sinal de entrada da função no tempo, comparando-a com um valor pré-determinado. Além disso, existem outras técnicas mais elaboradas que ganharam espaço graças ao avanço conseguido pelos computadores tais como análise espectral, correlação, médias, análise cepstral, envoltória, etc (Leite et al, 2014).…”

Section: Apresentação Do Sistema Para Detecção De Defeitosunclassified

Aplicação De FFT Para a Detecção De Defeitos Em Máquinas Elétricas

Bonaldi¹,

Oliveira²,

Lambert‐Torres³

et al. 2015

Anais Do Congresso Nacional De Matemática Aplicada À Indústria

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“…Stator faults include stator windings defects, stator core defects, and stator frame defects [8]. Mechanical faults include bearing damage and shaft defects [9]. Literature has reported that stator winding faults are one of the common faults in an electric machine, with approximately 36% of faults in an induction machine due to stator winding faults [8].…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, different types of signals have been used as an indicator for motor fault diagnosis. These indicators include magnetic flux, vibration, stator current (negative and zero sequence or third harmonic component), zero sequence component of voltage, thermal image processing, instantaneous real power and reactive power, phase shift between stator and supply voltages, and acoustic noise [9,11,[15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Moreover, many researchers used the instantaneous torque as a fault indicator in induction motors [18,21,[29][30][31].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors

2018

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Induction motors constitute the largest proportion of motors in industry. This type of motor experiences different types of failures, such as broken bars, eccentricity, and inter-turn failure. Stator winding faults account for approximately 36% of these failures. As such, condition monitoring is used to protect motors from sudden breakdowns. This paper proposes the use of neural networks as an efficient diagnostic tool for estimating the percentage of stator winding shorted turns in three-phase induction motors. A MATLAB-based model was developed and simulated under different fault-load combination cases for different sizes of motors. The motor's developed electromechanical torque was selected as a fault indicator. For the design and training of the neural network, the mean, variance, max, min, and F120 time based on statistical and frequency-related features were found to be very distinct for correlating the captured electromechanical torque with its corresponding percentage of shorted turns. In the training phase of the neural network, five different motors were used and are referred to as seen motors. On the other hand, for testing the efficiency of the developed diagnostic tool, the electromechanical torque under different fault-load combination cases, previously never seen from the first five motors and those of two new motors (referred to as unseen), was used. Testing results revealed accuracy in the range of 88-99%.

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Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current

Cited by 231 publications

References 41 publications

Effects of the Slot Harmonics on the Stator Current in an Induction Motor with Bearing Fault

Effects of the Slot Harmonics on the Stator Current in an Induction Motor with Bearing Fault

Aplicação De FFT Para a Detecção De Defeitos Em Máquinas Elétricas

An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors

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