High-Resolution Parameter Estimation Method to Identify Broken Rotor Bar Faults in Induction Motors

Kim, Yong‐Hwa; Youn, Young-Woo; Hwang, Don-Ha; Sun, Jong-Ho; Kang, Dae-Seong

doi:10.1109/tie.2012.2227912

Cited by 136 publications

(83 citation statements)

References 32 publications

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“…When an induction motor rotor bar is broken, the stator steady-state current has harmonic frequencies given by [15,17] = (1 2 ) ,…”

Section: Frequency Analysis Of Broken Rotor Barmentioning

confidence: 99%

“…Subspace methods such as a multiple signal classification (MUSIC) algorithm and an estimation of signal parameters via rotational invariance techniques (ESPRIT) have been applied to diagnose induction motor faults while overcoming the frequency resolution limitation of the FFT method in low signal-to-noise ratios (SNRs) [13][14][15][16][17]. The MUSIC and ESPRIT compute an autocorrelation matrix of the signals, estimate a frequency signal dimension order (FSDO), use signal and noise subspaces, and convert the fault detection problem into a generalized eigenvalue problem [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…The MUSIC and ESPRIT compute an autocorrelation matrix of the signals, estimate a frequency signal dimension order (FSDO), use signal and noise subspaces, and convert the fault detection problem into a generalized eigenvalue problem [13][14][15][16][17]. The MUSIC was applied to estimate the harmonic components in order to improve induction motor fault detection [13].…”

Section: Introductionmentioning

confidence: 99%

“…Using the motor leakage flux spectral analysis, the MUSIC is applied to identify BRB faults in induction motors [16]. The ESPRIT with the zooming technique has been proposed to estimate both the frequencies and amplitudes of BRB fault harmonics using the stator current analysis [17]. However, the difference in the spectrum of healthy motors and those with BRB faults determined using the zoom MUSIC (ZMUSIC) [15] or zoom ESPRIT (ZESPRIT) [17] is not obvious when the load level is close to no-load condition.…”

Section: Introductionmentioning

confidence: 99%

“…However, the difference in the spectrum of healthy motors and those with BRB faults determined using the zoom MUSIC (ZMUSIC) [15] or zoom ESPRIT (ZESPRIT) [17] is not obvious when the load level is close to no-load condition. To overcome this limitation, this paper proposes another fault diagnostic index using the result of the FSDO estimator, which is needed to perform the ZMUSIC [15] or ZESPRIT [17].…”

Section: Introductionmentioning

confidence: 99%

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Robust Diagnosis Algorithm for Identifying Broken Rotor Bar Faults in Induction Motors

Hwang¹,

Youn²,

Sun³

et al. 2014

Journal of Electrical Engineering and Technology

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-This paper proposes a new diagnosis algorithm to detect broken rotor bars (BRBs) faults in induction motors. The proposed algorithm is composed of a frequency signal dimension order (FSDO) estimator and a fault decision module. The FSDO estimator finds a number of fault-related frequencies in the stator current signature. In the fault decision module, the fault diagnostic index from the FSDO estimator is used depending on the load conditions of the induction motors. Experimental results obtained in a 75 kW three-phase squirrel-cage induction motor show that the proposed diagnosis algorithm is capable of detecting BRB faults with an accuracy that is superior to a zoom multiple signal classification (ZMUSIC) and a zoom estimation of signal parameters via rotational invariance techniques (ZESPRIT).

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“…When an induction motor rotor bar is broken, the stator steady-state current has harmonic frequencies given by [15,17] = (1 2 ) ,…”

Section: Frequency Analysis Of Broken Rotor Barmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust Diagnosis Algorithm for Identifying Broken Rotor Bar Faults in Induction Motors

Hwang¹,

Youn²,

Sun³

et al. 2014

Journal of Electrical Engineering and Technology

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Rotor fault diagnosis in induction motors by the matrix pencil method and support vector machine

Chahine

2018

Int Trans Electr Energ Syst

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Summary This paper proposes a high‐resolution motor‐current‐signature‐analysis method and a supervised learning algorithm for the detection and classification of broken rotor bars and broken end‐ring connectors in 3‐phase induction motors. Signature analysis relies on the matrix pencil method (MPM), a well‐known model‐based parameter estimation technique, to extract representative features or signatures from stator current signals. Extracted feature vectors are subsequently used to train off‐line a support vector machine classifier. Once trained, the classifier is tested on a benchmark dataset of simulated stator current signals representing healthy and faulty rotors with the aim of classifying the underlying motor condition. The obtained results validate the matrix pencil method as a feature extraction method and show that the trained classifier achieves a 100% success rate in identifying the number of broken bars and connectors. Moreover, the advantages of the matrix pencil method over fast Fourier transform are demonstrated using experimental data.

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Monitoring and Fault Diagnosis of Induction Motors Mechanical Faults Using a Modified Auto-regressive Approach

Aimer

Boudinar

Khodja

et al. 2018

Advanced Control Engineering Methods in Electrical Engineering Systems

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High-Resolution Parameter Estimation Method to Identify Broken Rotor Bar Faults in Induction Motors

Cited by 136 publications

References 32 publications

Robust Diagnosis Algorithm for Identifying Broken Rotor Bar Faults in Induction Motors

Robust Diagnosis Algorithm for Identifying Broken Rotor Bar Faults in Induction Motors

Rotor fault diagnosis in induction motors by the matrix pencil method and support vector machine

Monitoring and Fault Diagnosis of Induction Motors Mechanical Faults Using a Modified Auto-regressive Approach

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