Broken Rotor Bars Detection in Induction Motors Running at Very Low Slip Using a Hall Effect Sensor

Dias, Cleber Gustavo; Pereira, Fábio Henrique

doi:10.1109/jsen.2018.2827204

Cited by 55 publications

(32 citation statements)

References 41 publications

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“…As mentioned before, in this paper, the magnetic air gap disturbances have been used to evaluate the rotor condition. Other works have demonstrated the possibility to detect broken rotor bars using this kind of signal and a Hall sensor installed between two stator slots of the machine [19,38,39,44]. In [45], an array of Hall effect sensors was installed around the stator circumference inside the motor air gap to detect rotor bar damage and stator turn-to-turn shorts.…”

Section: Air Gap Disturbances Monitoring Using a Hall Effect Sensormentioning

confidence: 99%

“…The work published by [39] disclosed the use of some statistical data to perform the magnetic density evaluation of a signal produced by a Hall sensor installed inside an induction motor. In that case, the following features have been used: mean, root mean square, variance, kurtosis and skewness.…”

Section: Statistical Data Of Each Fuzzified Half-cyclementioning

confidence: 99%

“…In [39], three learning machine techniques (MLP, KNN and SVM) were used to detect and classify rotor faults also running at low slip. In this case, some frequency and statistical features have been used to evaluate the rotor condition in an acquistion time of 4 s for motor running at steady-state.…”

Section: Introductionmentioning

confidence: 99%

“…Although the works published by [39][40][41][42] have successfully used machine learning methods for electrical and/or mechanical failures diagnosis, they did not address the problem related to load variation or load oscillation for broken rotor bars detection.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy-Based Statistical Feature Extraction for Detecting Broken Rotor Bars in Line-Fed and Inverter-Fed Induction Motors

2019

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This paper presents the use of a fuzzy-based statistical feature extraction from the air gap disturbances for diagnosing broken rotor bars in large induction motors fed by line or an inverter. The method is based on the analysis of the magnetic flux density variation in a Hall Effect Sensor, installed between two stator slots of the motor. The proposed method combines a fuzzy inference system and a support vector machine technique for time-domain assessment of the magnetic flux density, in order to detect a single fault or multiple broken bars in the rotor. In this approach, it is possible to detect not only the existence of failures, but also its severity. Moreover, it is not necessary to estimate the slip of the motor, usually required by other methods and the damaged rotor detection was also evaluated for oscillating load conditions. Thus, the present approach can overcome some drawbacks of the traditional MCSA method, particularly in operational cases where false positive and false negative indications are more frequently. The efficiency of this approach has been proven using some computational simulation results and experimental tests to detect fully broken rotor bars in a 7.5 kW squirrel cage induction machine fed by line and an inverter.

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Section: Air Gap Disturbances Monitoring Using a Hall Effect Sensormentioning

confidence: 99%

Section: Statistical Data Of Each Fuzzified Half-cyclementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fuzzy-Based Statistical Feature Extraction for Detecting Broken Rotor Bars in Line-Fed and Inverter-Fed Induction Motors

2019

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“…Several MCSA-based techniques have been proposed in literature for BRB fault detection, based on spectral analysis of sidebands of the power frequency or its harmonics using methods such as multiresolution Taylor-Kalman approach [5], low-frequency load torque oscillations [6], auto-regression analysis [7], autoregressive-spectrum analysis [8] and high-resolution parameter estimation [9]. However, similar sidebands can also occur in spectral maps of healthy motors due to rotor asymmetries caused by manufacturing/assembly tolerances and connection coupling.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive EMD Technique for Induction Motor Fault Detection

Mahmud¹,

Wang²

2019

JSIP

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Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. An adaptive empirical mode decomposition (EMD) technology is proposed in this paper for rotor bar fault detection in IMs. As the characteristic fault frequency will change with operating conditions related to load and speed, the proposed adaptive EMD technique correlates fault features over different frequency bands and intrinsic mode function (IMF) sidebands. The adaptive EMD technique uses the first IMF to detect the fault type and the second IMF as an indicator to predict the fault severity. It can overcome the problems of the sensitivity of sideband frequencies related to the speed and load oscillations. The effectiveness of the proposed adaptive EMD technique is verified by experimental tests under different motor conditions.

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Artificial Neural Networks Technique to Detect and Locate an Interturn Short-Circuit Fault in Induction Motor

Bensaoucha

Ameur

Bessedik

et al. 2018

Renewable Energy for Smart and Sustainable Cities

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Broken Rotor Bars Detection in Induction Motors Running at Very Low Slip Using a Hall Effect Sensor

Cited by 55 publications

References 41 publications

Fuzzy-Based Statistical Feature Extraction for Detecting Broken Rotor Bars in Line-Fed and Inverter-Fed Induction Motors

Fuzzy-Based Statistical Feature Extraction for Detecting Broken Rotor Bars in Line-Fed and Inverter-Fed Induction Motors

An Adaptive EMD Technique for Induction Motor Fault Detection

Artificial Neural Networks Technique to Detect and Locate an Interturn Short-Circuit Fault in Induction Motor

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