A Novel Approach for Broken Bar Fault Diagnosis in Induction Motors Through Torque Monitoring

Gyftakis, Konstantinos N.; Spyropoulos, Dionysios V.; Kappatou, J. C.; Mitronikas, Epaminondas D.

doi:10.1109/tec.2013.2240683

Cited by 105 publications

(48 citation statements)

References 23 publications

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“…For the measurement of the torque, either direct or indirect methods can be used. The direct method uses torque sensors as given in [21,29]. In [47,48], the torque was measured with the torque meter DATAFLEX ® 22/50 (KTR systems, Rheine, Germany).…”

Section: Neural Network Selection Training and Testingmentioning

confidence: 99%

“…It is important to mention that there are differences in the instantaneous torque response under broken bar and inter-turn faults [30]. In addition, based on previous studies [18,21], the time response of torque at the steady state is different under these two types of faults, while the prominent frequency components during faults are also different. These differences will help in discriminating between the two types of faults.…”

Section: Introductionmentioning

confidence: 97%

“…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]. It is important to mention that there are differences in the instantaneous torque response under broken bar and inter-turn faults [30].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Neural Network Selection Training and Testingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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

2018

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“…The presence of a fault in an electric motor associated with a critical industrial process leads to emergency interruptions [9], which causes production process losses [8,10,11]. Therefore, early fault detection is required to identify incipient fault presence in induction motors, and consequently to avoid these problems [6,12].…”

Section: Introductionmentioning

confidence: 99%

Broken Bar Diagnosis for Squirrel Cage Induction Motors Using Frequency Analysis Based on MCSA and Continuous Wavelet Transform

Granda

Aguilar

Arcos-Avilés

et al. 2017

MCA

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Abstract:The importance of early fault detection in electric motors has attracted the attention of research groups, as the detection of incipient faults can prevent damage spreading and increase the lifetime of the motor. At present, studies have focused their attention on optimization procedures used for fault detection in induction machines to achieve a quick and easy-to-interpret assessment at an industrial level. This paper proposes an alternative approach based on the Continuous Wavelet Transform (CWT) for broken bar diagnosis in squirrel cage induction motors. This work uses the Motor Current Signature Analysis (MCSA) method to acquire the current signal of the induction motor. The novelty of this study lies in broken bar detection in electric machines operating at non-load by analyzing variations in the spectrum of the motor's current signal. This way, the faults are presented as oscillations in the current signal spectrum. Additionally, a quantification of broken bars for the same type of motors operating at full-load is performed in this study. An experimental validation and the comparison with the Fast Fourier Transform (FFT) technique are provided to validate the proposed technique.

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“…Other researchers have been investigated the influence of broken bar fault on the instantaneous power, activereactive power, instantaneous power factor, torque, external magnetic field analysis, current Park components, inverter input current analysis and supply voltage modulation when the machine is supplied by electrical power converters [4,5,6,7,8,9,10,11,12,13]. These techniques detect the broken bar fault at frequencies close to the dc component in the spectrum 2ksf s .…”

Section: Introductionmentioning

confidence: 99%

802.11s QoS Routing for Telemedicine Service

Satria

Yunus

Supriyanto

2014

IJECE

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The majority of research is about the detection of broken bar fault in threephase induction motor at high slip. Thus, it becomes very interesting and demanding to detect faults in case of low slip range. In this study, a novel investigation of broken bar faults using instantaneous power is presented. The method is based on calculations and frequency analysis of partial and total instantaneous power under low slip range. The used model of squirrel cage induction machine takes into account the geometry and winding layout. This model will be used to analyze the impact of broken bar on instantaneous power spectrum. The proposed ideas in this paper are verified experimentally. The results show that broken bar fault can be more reliably detected under low slip range when using a large frequency area of both partial and total instantaneous power spectrums.

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A Novel Approach for Broken Bar Fault Diagnosis in Induction Motors Through Torque Monitoring

Cited by 105 publications

References 23 publications

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

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

Broken Bar Diagnosis for Squirrel Cage Induction Motors Using Frequency Analysis Based on MCSA and Continuous Wavelet Transform

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