Analysis Techniques for Detection of IM Broken Rotor Bars After Supply Disconnection

Cupertino, Francesco; Vanna, E. de; Salvatore, L.; Stasi, S.

doi:10.1109/tia.2004.824432

Cited by 47 publications

(12 citation statements)

References 13 publications

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“…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%

“…The MUSIC was applied to estimate the harmonic components in order to improve induction motor fault detection [13]. The MUSIC and short-time MUSIC were used for the analysis of the space-vector of voltages induced in stator windings, after supply disconnection, to detect BRBs faults in induction machines [14]. The MUSIC algorithm with zooming methods was proposed to detect fault sensitive frequencies based on a frequency analysis of the stator current [15].…”

Section: Introductionmentioning

confidence: 99%

See 2 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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-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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…[5] for an overview of recent developments. Subspace techniques (MU-SIC [6]) has previously been used in [7], [8], but DFT-methods still appear to be the prevalent diagnostic tool in the literature [5]. Recently a state-space model capturing physical parameters of the motor has been proposed in [9], where a motor failure can be detected and diagnosed from estimated model parameters.…”

Section: Introductionmentioning

confidence: 99%

Subspace-based frequency estimation utilizing prior information

Wirfält

Bouleux

Jansson

et al. 2011

2011 IEEE Statistical Signal Processing Workshop (SSP)

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In certain frequency estimation applications one or more of the underlying frequencies are known. For example, in rotary machines the known frequency may be a strong network frequency masking important closely spaced frequencies. Being able to include this information in the design of the estimator can be expected to improve the performance when estimating such closely spaced frequencies. We present a framework to include such prior information in a class of subspace-based estimators. Through Monte Carlo simulations and real-data applications we show the usefulness of our approach.

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“…The methods of spectral analysis are used on electrical stationary quantities when the electrical machine is connected to the network or to a frequency converter. The case of nonstationarity has also been used in the method of the disconnection of power to the motor [6], where the spectrum of the residual emf in the stator windings is used for the detection of rotor and stator faults. Monitoring of characteristic frequencies allow the characterization of many faults in the induction machine and this, sometimes independently of the amplitude of the measured signals (the occurrence or the modification of a characteristic frequency determines the presence of the fault and not the amplitude of the measured signal).…”

Section: Introductionmentioning

confidence: 99%

On-line Faults Signature Monitoring Tool for Induction Motor Diagnosis

Medoued¹,

Lebaroud²,

Boukadoum³

et al. 2010

Journal of Electrical Engineering and Technology

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-The monitoring and the diagnosis of the faults in induction motors starting from the stator current are very interesting, since it is an accessible and measurable quantity. The spectral analysis of the stator current makes it possible to highlight the characteristic frequencies of the faults but in a wide frequency range depending on half the sampling frequency, making it very difficult to monitor on-line the faults. In order to facilitate the use of the relevant frequencies of machine faults we proposed the extraction of the frequency components using two methods, namely, the amplitude and the instantaneous frequency. The theoretical bases of these methods were presented and the results were validated on a test bench with an induction motor of 5.5 kw.

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Analysis Techniques for Detection of IM Broken Rotor Bars After Supply Disconnection

Cited by 47 publications

References 13 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

Subspace-based frequency estimation utilizing prior information

On-line Faults Signature Monitoring Tool for Induction Motor Diagnosis

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