Stator current based indicators for bearing fault detection in synchronous machine by statistical frequency selection

Obeid, Ziad; Poignant, Sylvain; Regnier, Jérémi; Maussion, Pascal

doi:10.1109/iecon.2011.6119621

Cited by 20 publications

(15 citation statements)

References 16 publications

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“…A bearing has 8 balls with an approximate diameter d of 12 mm and a contact angle of 0 • . Bearing faults are obtained by simply drilling holes in different parts [24]. In order to study the load influence, the induction machine was operated with various load levels ranging from 0 W to 400 W. The measured quantities for off-line bearing fault detection were the line-currents.…”

Section: B Experimental Resultsmentioning

confidence: 99%

A parametric spectral estimator for faults detection in induction machines

Bouchikhi¹,

Choqueuse²,

Benbouzid³

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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Current spectrum analysis is a proven technique for fault diagnosis in electrical machines. Current spectral estimation is usually performed using classical techniques such as periodogram (FFT) or its extensions. However, these techniques have several drawbacks since their frequency resolution is limited and additional post-processing algorithms are required to extract a relevant fault detection criterion. Therefore, this paper proposes a new parametric spectral estimator that fully exploits the faults sensitive frequencies. The proposed technique is based on the maximum likelihood estimator and offers high-resolution capabilities. Based on this approach, a fault criterion is derived for detecting several fault types. The proposed faults detection technique is assessed using simulations, issued from a coupled electromagnetic circuits approach-based simulation tool. It is afterwards validated using experiments on a 0.75-kW induction machine test bed for the particular case of bearing faults.

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Section: B Experimental Resultsmentioning

confidence: 99%

A parametric spectral estimator for faults detection in induction machines

Bouchikhi¹,

Choqueuse²,

Benbouzid³

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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“…It is easy to point out in these figures that, depending on the operating speed (and certainly on the bearing itsel1), the SK indicator is not fully reliable. It truly reflects the bearing fault at 14 100 rpm while it varies for 10 000 rpm, for other reasons such as DC bus variation or a circlip fault as depicted in [19 ]. Then, a change is made in the calculation of the indicator.…”

Section: Energy Based Degradation Indicatorsmentioning

confidence: 98%

Experimental comparison between diagnostic indicators for bearing fault detection in synchronous machine by spectral Kurtosis and energy analysis

Obeid

Picot

Poignant

et al. 2012

IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society

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In this paper, some indicators are developed for efficient detection of bearinl?; defaults in hil?;h speed synchronous machines. These indicators are based on the analysis of stator current. As bearinl?; defect sil?;natures can be tracked throul?;h amplitude increase of some current harmonics, two specific indicators have been built-based on energy considerations and on the Spectral Kurtosis analysis. These indicators are tested on a real industrial fan equipped with ceramic balls, in its environment. Several measurements for different operating points are tested to validate the approach and to its robustness during long time tests. From an experimental comparison between a healthy fan and another with damaged bearinl?;s, a frequency selection is performed to identify the frequency ranl?;es where the energy is the most sensitive to the considered faults. This actuator is used in an air conditioning fan in aeronautic applications.

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“…Obeid et al in [32] used instead stator current developed a degradation indicator, by calculating the accumulation of energies around the expected fault frequencies.…”

Section: Classifiers Once Features Have Been Extracted the Issuementioning

confidence: 99%

Response of electrical drives to gear and bearing faults — Diagnosis under transient and steady state conditions

Strangas

2013

2013 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)

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Bearing and gear faults in systems using electrical drives share many commonalities and have some differences in they way that they can be detected. They both cause a mechanical impulse at a frequency that is related to the speed of the rotor. For neither is it possible to develop a state-space model relating the fault and its location and severity to the measured outputs. The measurements therefore require extensive processing to extract features that are related to a fault and to categorize it. Measurements physically close to the fault location (e.g. vibrations) are generally more useful to accurately determine this fault than measurements away from it (e.g. currents and voltage at the drive). Models of faults include fatigue (e.g. Paris model) and degradation due to bearing currents.In the recent past the techniques to identify these faults have been refined, tested extensively, and compared. They typically include signal conditioning, feature extraction (in the time and time-frequency domain) and categorization, which includes fault identification. Failure prognosis and use of multiple sensors are possible future directions of research to produce reliable estimated of condition and facilitate health management.

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Stator current based indicators for bearing fault detection in synchronous machine by statistical frequency selection

Cited by 20 publications

References 16 publications

A parametric spectral estimator for faults detection in induction machines

A parametric spectral estimator for faults detection in induction machines

Experimental comparison between diagnostic indicators for bearing fault detection in synchronous machine by spectral Kurtosis and energy analysis

Response of electrical drives to gear and bearing faults — Diagnosis under transient and steady state conditions

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Stator current based indicators for bearing fault detection in synchronous machine by statistical frequency selection

Cited by 20 publications

References 16 publications

A parametric spectral estimator for faults detection in induction machines

A parametric spectral estimator for faults detection in induction machines

Experimental comparison between diagnostic indicators for bearing fault detection in synchronous machine by spectral Kurtosis and energy analysis

Response of electrical drives to gear and bearing faults &#x2014; Diagnosis under transient and steady state conditions

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Product

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Response of electrical drives to gear and bearing faults — Diagnosis under transient and steady state conditions