Characteristics Analysis of Induction Motor with Broken Rotor Bars at Steady State

Kawase, Yoshihiro; Yamaguchi, Tadashi; Uozumi, H.; Kawano, Hiroshi; Watanabe, Masashi; Kusadome, Taizo

doi:10.23919/icems50442.2020.9290789

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…A broken rotor bar is one of the common faults in squirrel cage IMs. The BRB fault may be caused by several factors, such as thermal stress, mechanical stress, environmental stress, residual stress, or dynamical stress [2,3]. The approximation rate of fault occurrence is higher for motors rated above 4 kV; about 5% of the fault occurrence rate is due to broken rotor bars in squirrel cage induction motors [3,4].…”

Section: Introductionmentioning

confidence: 99%

Detection of Broken Rotor Bars in Cage Induction Motors Using Machine Learning Methods

Chisedzi,

Muteba

2023

Sensors

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In this paper, the performance of machine learning methods for squirrel cage induction motor broken rotor bar (BRB) fault detection is evaluated. Decision tree classification (DTC), artificial neural network (ANN), and deep learning (DL) methods are developed, applied, and studied to compare their performance in detecting broken rotor bar faults in squirrel cage induction motors. The training data were collected through experimental measurements. The BRB fault features were extracted from measured line-current signatures through a transformation from the time domain to the frequency domain using discrete Fourier Transform (DFT) of the frequency spectrum of the current signal. Eighty percent of the data were used for training the models, and twenty percent were used for testing. A confusion matrix was used to validate the models’ performance using accuracy, precision, recall, and f1-scores. The results evidence that the DTC is less load-dependent, and it has better accuracy and precision for both unloaded and loaded squirrel cage induction motors when compared with the DL and ANN methods. The DTC method achieved higher accuracy in the detection of the magnitudes of the twice-frequency sideband components induced in stator currents by BRB faults when compared with the DL and ANN methods. Although the detection accuracy and precision are higher for the loaded motor than the unloaded motor, the DTC method managed to also exhibit a high accuracy for the unloaded current when compared with the DL and ANN methods. The DTC is, therefore, a suitable candidate to detect broken rotor bar faults on trained data for lightly or thoroughly loaded squirrel cage induction motors using the characteristics of the measured line-current signature.

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