Broken bar detection in induction motor via wavelet transformation

Abbaszadeh, K; Milimonfared, Jafar; Haji, Moosa Moghimi; Toliyat, H.A.

doi:10.1109/iecon.2001.976461

Cited by 55 publications

(24 citation statements)

References 11 publications

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“…Zhongming Ye and Bin Wu [6] and K. Abbaszadeh and friends [4] present the simulation of induction motors faults of broken rotor bar by winding function method. Wavelet packet decomposition is used to extract fault signature of stator current.…”

Section: Introductionmentioning

confidence: 99%

“…A large amount of research has been directed toward using the stator current spectrum to detect rotor faults associated with broken rotor bars [4][5][6][7][8][9][10][11][12][13][14][15]. The early detection of rotor cage faults is very important because in the case of a broken rotor bar, the changes in torque characteristic are usually still not noticeable without measuring devices [11][12][13][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…During recent years, there has been a substantial amount of research to provide condition monitoring techniques for ac induction motors based on analyzing stator current signals [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. A large amount of research has been directed toward using the stator current spectrum to detect rotor faults associated with broken rotor bars [4][5][6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Automatic detection and classification of rotor cage faults in squirrel cage induction motor

Arabaci

Bilgin

2009

Neural Comput & Applic

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The detection of broken rotor bars and broken end-ring in three-phase squirrel cage induction motors by means of improved decision structure. The structure consists of current signal analysis (CSA), Artificial Neural Network (ANN) and diagnosis algorithm. Effects of broken bars and end-ring on current signal and feature extraction are in the CSA. The rotor cage faults are classified by using ANN. And result matrixes of ANN are considered two different ways for diagnosis. Then the diagnoses are compared with each other. In this study six different rotor faults, which are one, two, three broken bars, bar with high resistance, broken end-ring and healthy rotor, are investigated. The effects of different rotor faults on current spectrum, in comparison with other fault conditions, are investigated by analyzing side-bands in current spectrum. To reduce bad effects of changing of distance between the side-band and main component on the detection and classification of the faults, the spectrum is achieved with low definition. Thus, the improved decision structure diagnoses faulted rotors with 100% accuracy and classified rotor faults 98.33% accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic detection and classification of rotor cage faults in squirrel cage induction motor

Arabaci

Bilgin

2009

Neural Comput & Applic

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“…Mallat's pyramidal algorithm based on convolutions with quadratic mirror filters is a fast method similar to FFT for signal decomposition of the original signal in an orthonormal wavelet basis or as a decomposition of the signal in a set of independent frequency bands. The independence is due to the orthogonality of the wavelet function [3].…”

Section: Introductionmentioning

confidence: 99%

Application of Time-Series Data Mining for Fault Diagnosis of Induction Motors

Bae

Kim

et al. 2005

Computational Science and Its Applications – ICCSA 2005

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Abstract. The motor is the workhorse of industries. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This paper introduces a technique to detect faults in induction motors. Stator currents are measured by current meters and stored by time domain. The time domain is not suitable for representing current signals, so the frequency domain is used to display the signals. Fourier transform is used to convert the signals onto frequency domain. After the signals have been converted, the features of the signals are extracted by the signal processing methods like the wavelet analysis, spectrum analysis, and other methods. The discovered features are entered to a pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, or other models. This paper describes the results of detecting fault using Fourier and wavelet analysis.

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“…A novelty to the weighting function was introduced by (Ordaz Moreno et al, 2008). (Abbas zadeh et al, 2001) presented a novel approach to detect the broken bar fault in squirrel cage induction motors. Two 3 HP induction motors with cast aluminum rotor bars were employed for this experiment.…”

Section: Introductionmentioning

confidence: 99%