Novel Methodology for Online Half-Broken-Bar Detection on Induction Motors

Rangel-Magdaleno, José; Romero-Troncoso, René de Jesús; Osornio-Ríos, Roque Alfredo; Cabal-Yépez, Eduardo; Contreras-Medina, Luis Miguel

doi:10.1109/tim.2009.2012932

Cited by 90 publications

(42 citation statements)

References 17 publications

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“…In order to overcome this drawback improved MCSA have been proposed [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] using a joined time-frequency transformation on the motor stator currents and WT, and featuring extraction and fault diagnosis, even under variable load conditions. However, when using WT it is not easy to define a simple algorithm to develop an automatic fault-detection system due to the predetermined frequency analysis bands associated with discrete filter banks of the transformation.…”

Section: Mcsa Analysismentioning

confidence: 99%

“…The voltage relationship between rotor and stator, torque, speed, and rotor position is well defined in literature [23], and is used here as a basis of faulty motor model.…”

Section: Fault Simulation and Detectionmentioning

confidence: 99%

“…[23] a novel methodology for half-broken-bar detection, which combines current and vibration analysis by correlating the signal spectra to enhance detectability for mechanically loaded and unloaded operating conditions of the motor, which the other isolated techniques are unable to detect.…”

Section: Introductionmentioning

confidence: 99%

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On-line fault detection method for induction machines based on signal convolution

Cusidó¹,

Romeral²,

Espinosa³

et al. 2010

Euro. Trans. Electr. Power

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SUMMARYA new technique for induction motor fault detection and diagnosis is presented. This technique, which has been experimentally verified in stationary and non-stationary motor conditions, is based on the convolution of wavelet-based functions with motor stator currents. These functions are tuned to specific fault frequencies taking into account motor speed and load torque, thus considering variable operation conditions of the motor. Based on this technique an automatic system for fault diagnosis is also presented, which is suited for easy software implementation Q3 .

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Section: Mcsa Analysismentioning

confidence: 99%

“…The voltage relationship between rotor and stator, torque, speed, and rotor position is well defined in literature [23], and is used here as a basis of faulty motor model.…”

Section: Fault Simulation and Detectionmentioning

confidence: 99%

See 1 more Smart Citation

On-line fault detection method for induction machines based on signal convolution

Cusidó¹,

Romeral²,

Espinosa³

et al. 2010

Euro. Trans. Electr. Power

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show abstract

“…To address this issue, in [1] using vibration signal of motor, a half-broken-bar detection method is proposed. They have implemented a decision tree in a fieldprogrammable gate array which compares the regions of signal that represents a broken bar or a half-broken bar.…”

Section: Introductionmentioning

confidence: 99%

Time to Fault Minimization for Induction Motors Using Wavelet Transform

Ghods

Lee

2014

Intelligent Computing Methodologies

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Abstract. Time to Fault (TTF) is an important parameter that measures how long it takes that a fault detection algorithm successfully recognizes defect in the motor. If TTF is too long, severe damages can happen to the motor. In this paper, authors try to minimize TTF using Discrete Wavelet Transform (DWT); in other words, the output signals derived from the motor due to an existing fault are analyzed and decomposed in frequency-domain. It will be proved that even though there are n levels for decomposing the signal with 2 n data samples, but after a specific level, the fault characteristics will disappear. This happens because of sporadic form of the signal. Thus, we can finish the analysis in a lower level where all characteristics for fault can be seen. This reduces TTF and consequently possible damages considerably.

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“…In MCSA, the signal conditioning stage may include windowing [4], filtering, frequency displacement [5], elimination of negative frequencies [6], etc. Spectral analysis may be performed with a variety of mathematical tools, such as the Fourier transform (FT) [7]- [10], multiple signal classification [8], maximum covariance methods [11], [12], and others [13]. Finally, a threshold level is established that is based on either the amplitude [7], [14] or the phase of the characteristic fault harmonics, as given in (1) and (2), to diagnose the presence or the absence of a fault.…”

mentioning

confidence: 99%

Diagnosis of Induction Motor Faults in the Fractional Fourier Domain

Pineda-Sánchez

Riera-Guasp

Antonino-Daviu

et al. 2010

IEEE Trans. Instrum. Meas.

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Abstract-Motor current signature analysis (MCSA) is a well-established method for the diagnosis of induction motor faults. It is based on the analysis of the spectral content of a motor current, which is sampled while a motor runs in steady state, to detect the harmonic components that characterize each type of fault. The Fourier transform (FT) plays a prominent role as a tool for identifying these spectral components. Recently, MCSA has also been applied during the transient regime (TMCSA) using the whole transient speed range to create a unique stamp of each harmonic as it evolves in the time-frequency plane. This method greatly enhances the reliability of the diagnostic process compared with the traditional method, which relies on spectral analysis at a single speed. However, the FT cannot be used in this case because the fault harmonics are not stationary signals. This paper proposes the use of the fractional FT (FrFT) instead of the FT to perform TMCSA. This paper also proposes the optimization of the FrFT to generate a spectrum where the frequency-varying fault harmonics appear as single spectral lines and, therefore, facilitate the diagnostic process. A discrete wavelet transform (DWT) is used as a conditioning tool to filter the motor current prior to its processing by the FrFT. Experimental results that are obtained with a 1.1-kW three-phase squirrel-cage induction motor with broken bars are presented to validate the proposed method.

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Novel Methodology for Online Half-Broken-Bar Detection on Induction Motors

Cited by 90 publications

References 17 publications

On-line fault detection method for induction machines based on signal convolution

On-line fault detection method for induction machines based on signal convolution

Time to Fault Minimization for Induction Motors Using Wavelet Transform

Diagnosis of Induction Motor Faults in the Fractional Fourier Domain

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