Automatic three-phase squirrel-cage induction motor test assembly for motor thermal behaviour studies

Filho, Ernesto Ruppert; Riehl, Rudolf Ribeiro; Avolio, Edwin

doi:10.1109/isie.1994.333157

Cited by 14 publications

(6 citation statements)

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“…Corresponding to the above-mentioned faults, many techniques have been proposed for motor faults detection and diagnosis. These techniques include vibration monitoring, motor current signature analysis (MCSA) [3][4][5][6], electromagnetic field monitoring [7], chemical analysis, temperature measurability [8,9], infrared measurement, acoustic noise analysis [10], and partial discharge measurement [11,12]. Among these methods, vibration analysis and current analysis are the most popular due to their easy measurability, high accuracy, and reliability.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

Han

Yang

Choi

et al. 2006

International Journal of Rotating Machinery

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This paper proposes an online fault diagnosis system for induction motors through the combination of discrete wavelet transform (DWT), feature extraction, genetic algorithm (GA), and neural network (ANN) techniques. The wavelet transform improves the signal-to-noise ratio during a preprocessing. Features are extracted from motor stator current, while reducing data transfers and making online application available. GA is used to select the most significant features from the whole feature database and optimize the ANN structure parameter. Optimized ANN is trained and tested by the selected features of the measurement data of stator current. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origins on the induction motors. The results of the test indicate that the proposed system is promising for the real-time application.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

Han

Yang

Choi

et al. 2006

International Journal of Rotating Machinery

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“…In [4][5][6][7][8], the literature use a sensing coil, such as Hall transducer or Rogowski coil, to measure the variations of magnetic flux in induction motors. A few studies are based on the operating performances of broken rotor bars, such as voltage [9,10], current [11][12][13], vibration [14,15], noise [16,17], temperature [18], and electromagnetic torque [19]. The aforementioned methods are proposed for the rotor bars malfunction where the induced voltages and currents in the rotor bars are incorrect [20,21].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Defective Rotor Bars in Induction Motors

et al. 2020

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This paper proposes a diagnosis method, combining signal analysis and classification models, to the rotor defect problems of motors. Two manufacture technologies, nonmagnetic high-temperature resistant ceramic adhesive and electrical discharge machining (EDM), are applied to make testing samples, including blowhole and perforation defects of rotor bars in this study. The typical multiresolution analysis (MRA) model is used to analyze acquired source current signals of motors. The features are extracted from the signals of each column of MRA-matrix, including maximum, mean, standard deviation, root-mean-square, and summation. The typical back-propagation neural network (BPNN) model is used to diagnose the rotor bar defects of motors, and then the various signal-to-noise ratio (SNR) of white Gaussian noise (WGN), 30, 25, and 20 dB, are added to the signals to verify the robustness of the proposed method. The results show the availability of the proposed method to diagnose the rotor bar defects of motors.

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“…• Process-history data-based methods [5] such as signal processing based on chemical analysis, temperature [6], partial discharge [7] or vibration signal measurement. • Knowledge based methods such as Artificial Neural Networks, genetic algorithms [8], fuzzy logic [9].…”

Section: Introductionmentioning

confidence: 99%

Robust Fault Diagnosis and Load Torque Estimation in Electrical Drives Using Analytical Redundancy Relations and Sliding Mode Observer

Medjmadj

Mostefai

2018

IJEEI

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Electrical drives are more and more used in transportation applications and industrial process because of their effective cost and high efficiency. They have become critical components and therefore any fault or failure affecting them may seriously endanger the users and/or induce huge financial costs. As a consequence, early detection and isolation of these faults is recommended if not mandatory to enhance the safety, the availability, the reliability and improve the maintenance and the operational efficiency. Analytical Redundancy Relations (ARR) are used to compute to structured residuals on which the diagnosis is based. To smooth the residuals and enhance the fault detection capability, a sliding mode observer is developed to compute the derivatives. The analysis of the residuals reveals that they are Gaussian signals with a change in the mean. The comparison between the two-sigma and the two-sided CUSUM methods has shown that the latter is more efficient to compute the thresholds. Therefore with the two-sided CUSUM, the rotor resistance fault is detected with no false alarm. Moreover it is proved that the fault diagnosis is robust to the load torque variations. Beside, thanks to the residual, a load torque estimator is also developed. Extensive simulation results prove the validity of our approach.

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Automatic three-phase squirrel-cage induction motor test assembly for motor thermal behaviour studies

Cited by 14 publications

References 0 publications

Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

Diagnosis of Defective Rotor Bars in Induction Motors

Robust Fault Diagnosis and Load Torque Estimation in Electrical Drives Using Analytical Redundancy Relations and Sliding Mode Observer

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