Induction machine fault detection enhancement using a stator current high resolution spectrum

Bouchikhi, El Houssin El; Choqueuse, Vincent; Benbouzid, Mohamed; Charpentier, Jean-Frédéric

doi:10.1109/iecon.2012.6389267

Cited by 27 publications

(22 citation statements)

References 23 publications

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“…This criterion is based on the evaluation of frequency component amplitudes obtained with (19). It is an extension of the proposed criterion in [4]. The FSC is inspired from the total harmonic distortion (THD) of a signal, which is defined as the ratio of the sum of the powers of all harmonic components to the power of the fundamental frequency.…”

Section: A Proposed Fault Severity Criterionmentioning

confidence: 99%

“…Indeed, in [17], [18], a model order and spectral estimations based on MLE are proposed to detect induction machine fault frequency signatures. In stator current analysis, subspace techniques have been proposed to avoid the computational complexity inherent to multidimensional optimization of MLE [4], [19]- [26]. The subspace techniques include the MUSIC (MUltiple SIgnal Classification) and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) approaches.…”

mentioning

confidence: 99%

“…The proposed methods can graphically show the physical effect of a broken or partially-broken rotor bar. A modified version of the MUSIC algorithm has been developed in [4] to estimate the stator current spectrum. In [4], a fault detection criterion is proposed to detect faults.…”

mentioning

confidence: 99%

“…A modified version of the MUSIC algorithm has been developed in [4] to estimate the stator current spectrum. In [4], a fault detection criterion is proposed to detect faults. This criterion does not take into account the harmonic structure of the stator current.…”

mentioning

confidence: 99%

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Induction Machines Fault Detection Based on Subspace Spectral Estimation

Trachi

Elbouchikhi

Choqueuse

et al. 2016

IEEE Trans. Ind. Electron.

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International audience—The main objective of this paper is to detect faults in induction machines using a condition monitoring architecture based on stator current measurements. Two types of fault are considered: bearing and broken rotor bars faults. The proposed architecture is based on high-resolution spectral analysis techniques also known as subspace techniques. These frequency estimation techniques allow to separate frequency components including frequencies close to the fundamental one. These frequencies correspond to fault sensitive frequencies. Once frequencies are estimated, their corresponding amplitudes are obtained by using the Least Squares Estimator (LSE). Then, a fault severity criterion is derived from the amplitude estimates. The proposed methods were tested using experimental stator current signals issued from two induction motors with the considered faults. The experimental results show that the proposed architecture has the ability to efficiently and cost-effectively detect faults and identify their severity

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Section: A Proposed Fault Severity Criterionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Induction Machines Fault Detection Based on Subspace Spectral Estimation

Trachi

Elbouchikhi

Choqueuse

et al. 2016

IEEE Trans. Ind. Electron.

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“…Investigations using frequency domain features like power spectrum, power spectral density, periodograms etc. [5][6] relies on the differences in frequency characteristics of fault conditions [7]. These differences are non-significant and hence difficult to diagnose.…”

Section: Introductionmentioning

confidence: 99%

Effective Time Domain Features for Identification of Bearing Fault using LDA and NB Classifiers

Nayana¹,

Tjprc²

2018

IJMPERD

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On‐line detection and estimation of harmonics components in induction motors rotor fault through a modified Prony's method

Dehina

Boumehraz

Kratz

2020

Int Trans Electr Energ Syst

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Summary The diagnosis of electrical motor for broken rotor bars faults through frequency signatures of the stator current method has been widely explored. In fact, a high‐resolution spectrum analysis based on the multiple signal classification (MUSIC) method has been widely applied to detect induction machine faults for that purpose. Moreover, MUSIC is a powerful tool that extracts significant frequencies from the signal, but cannot give precise amplitude information of the fault indicator frequency. Nevertheless, the MUSIC algorithm takes a long‐time acquisition to find many frequencies. To solve these problems, the short‐time least square Prony's technique is developed. The technique is based on a modified Prony's method for the estimation of amplitudes certain harmonics and frequencies of broken rotor bar fault. The main objective in this context is the reduction in the samples of measurement data with shorter time to allow a clearer view of the fault harmonics components when the motor turns at low load. It allows determining the characteristic frequencies and amplitudes of the lateral bands around the fundamental frequency component with great precision. Additionally, using this proposed method for the magnitude modulation of stator current envelope spectrum is produced and induces current harmonics by the rotor fault at frequency. It is based on tracking the amplitude of harmonics of the stator current envelope. The effectiveness of the proposed method is tested in simulation and a real‐time implementation on experimental validation.

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Induction machine fault detection enhancement using a stator current high resolution spectrum

Cited by 27 publications

References 23 publications

Induction Machines Fault Detection Based on Subspace Spectral Estimation

Induction Machines Fault Detection Based on Subspace Spectral Estimation

Effective Time Domain Features for Identification of Bearing Fault using LDA and NB Classifiers

On‐line detection and estimation of harmonics components in induction motors rotor fault through a modified Prony's method

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