Motor bearing damage detection using stator current monitoring

Rolling bearing faults are generally slowly progressive; therefore the development of an effective diagnostic technique could be worth to detect such faults in their incipient phase and to prevent complete failure of the motor. The methods proposed in the literature for this purpose are mainly based on measuring and analyzing the vibration and current. Here, a novel technique based on the stray flux measurement in different positions around the electrical machine is proposed. The main advantages of this method are due to the simplicity and the flexibility of the custom flux probe with its amplification and filtering stage. The flux probe can be easily positioned on the machines and adapted to a wide range of power levels. The paper also reports an extensive survey on the stray flux based fault detection methods for induction motors, prior to introducing a novel sensor/diagnostic scheme.

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“…Therefore, studying the significant frequency components of the stray flux, stator or rotor asymmetries can be detected [36].…”

Section: The Proposed Fault Detection Methodsmentioning

confidence: 99%

Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

Frosini

Harlişca

Szabó

2015

IEEE Trans. Ind. Electron.

230

124

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“…Although there are several techniques for monitoring of bearing condition in induction machine, i.e., vibration, acoustic emission, and ultrasound, this section describes an approach based on electrical stator current analysis or current signature analysis. This approach has been gaining attention since bearing failure causes a modulation in electrical current signal, which can be identified in a similar way, as it is done in vibration analysis [15].…”

Section: Bearing Fault Diagnosis In Induction Machinementioning

confidence: 99%

“…It performs a spectral analysis of stator electrical current, which is usually monitored at one of three power supply phases. Studies related to mechanical faults effects on motor stator current mainly consider: load torque oscillations, rotating eccentricities, and air gap eccentricity [11,15,19].…”

Section: Motor Current Signature Analysis-mcsamentioning

confidence: 99%

Bearing Fault Detection in Induction Machine Using Squared Envelope Analysis of Stator Current

Leite¹,

daSilva²,

Torres³

et al. 2017

Bearing Technology

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In this chapter, motor current signature analysis based on squared envelope spectrum is applied in order to identify and to estimate the severity of outer race bearing faults in induction machine. This methodology is based on conventional vibration analysis techniques, however, it is, non-invasive, low cost, and easier to implement. Bearing fault detection and identification in induction machines is of utmost importance in order to avoid unexpected breakdowns and even a catastrophic event. Thus, bearing fault characteristic components are extracted combining summation of phase currents, prewhitening, spectral kurtosis and squared envelope spectrum analysis. Experimental results with a 0.37 W, 60 Hz, and three-phase induction machine demonstrated the methodology effectiveness.

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“…Faults can be due to electrical or mechanical failures. The most common failure in an asynchronous machine is bearing failure [1].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years much work has been carried out on the investigation of possible fault operations of electrical machines. The usual methods for detection of faults are based on vibration analysis [3-6,10-11] or on Motor current signature analysis (MCSA) [1][2][12][13].…”

Section: Introductionmentioning

confidence: 99%

On-line vibration monitoring of bearing faults in induction machine usin Cyclic Spectral Analysis

Tafinine

2016

MATEC Web Conf.

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Abstract.A robust signal processing method for developing an on-line bearing fault detection based on Cyclic Spectral Analysis is presented in this paper. The exploitation of cyclostationarity has recently been proved extremely fruitful to conceive high accuracy diagnostics tools. Cyclostationarity encompasses a subclass of nonstationary signals which exhibit some cyclical behaviour; it ideally fits the property of many rotating and reciprocating machine vibrations, due to the inherent periodic modulations that these sustain during operation. The traditional spectral analysis is not dedicated for non-stationary signals and for an on-line diagnosis. The proposed technique is tested with different bearing conditions, the results show that this diagnosis. The proposed technique is tested with different bearing conditions, the results show that this technique is especially useful for non-stationary features extraction; it gives a good basis for an integrated induction motor condition monitor.

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Motor bearing damage detection using stator current monitoring

Cited by 640 publications

References 4 publications

Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

Bearing Fault Detection in Induction Machine Using Squared Envelope Analysis of Stator Current

On-line vibration monitoring of bearing faults in induction machine usin Cyclic Spectral Analysis

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