The application of modern signal processing techniques to rotor fault detection and location within three phase induction motors

Burnett, R.O.; Watson, J.F.; Elder, S.

doi:10.1109/imtc.1995.515308

Cited by 30 publications

(22 citation statements)

References 1 publication

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“…Although this graph is deduced at steady state condition, its general shape fits well the description given in this section. The existence of a maximum in the sideband amplitude, during the second half of the startup was also establish by Burnett et al [19]; in that work the continuous wavelet transform was applied to experimental startup currents of faulty machines; from the observation of the resultant three-dimension graphs, the authors state that "as the non-stationary components travel back towards the 50 Hz component, in the later stages of the transient, the amplitude of the sidebands pass through a peak value". References [14,23] introduce a method for diagnosing bar breakages based on the analysis of the high level wavelet signals resulting from the DWT of the startup current.…”

Section: Fig 2evolution Of Left Sideband Harmonics During the Startsupporting

confidence: 64%

“…Burnet, Watson et al [19,20] evaluated the ability of different signal processing strategies for detecting the nonstationary components due to the rotor asymmetry in the startup current, concluding that the best results are obtained with the Wavelet decomposition. They introduced a simplified approach for detecting the left sideband by analyzing the output of a single pass-band filter, based on the convolution of the current signal with a Gaussian wavelet.…”

Section: Introductionmentioning

confidence: 99%

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The Use of the Wavelet Approximation Signal as a Tool for the Diagnosis of Rotor Bar Failures

Riera-Guasp

Antonino-Daviu

Roger‐Folch

et al. 2008

IEEE Trans. on Ind. Applicat.

126

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-The aim of this paper is to present a new approach for rotor bar failure diagnosis in induction machines. The method focuses on the study of an approximation signal resulting from the wavelet decomposition of the startup stator current.The presence of the left sideband harmonic is used as evidence of the rotor failure in most diagnosis methods based on the analysis of the stator current. Thus, a detailed description of the evolution of the left sideband harmonic during the startup transient is given in this paper; for this purpose, a method for calculating the evolution of the left sideband during the startup is developed and its results are physically explained. This paper also shows that the approximation signal of a particular level, obtained from the Discrete Wavelet Transform (DWT) of the startup stator current, practically reproduces the time evolution of the left sideband harmonic during the startup. The diagnosis method proposed here consists of checking if the selected approximation signal fits well the characteristic shape of the left sideband harmonic evolution described in the paper.The method is validated through laboratory tests. The results prove that it can constitute a useful tool for the diagnosis of rotor bar breakages.Index Terms-Broken rotor bars, fault diagnosis, startup transient, wavelet analysis.

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Section: Fig 2evolution Of Left Sideband Harmonics During the Startsupporting

confidence: 64%

Section: Introductionmentioning

confidence: 99%

The Use of the Wavelet Approximation Signal as a Tool for the Diagnosis of Rotor Bar Failures

Riera-Guasp

Antonino-Daviu

Roger‐Folch

et al. 2008

IEEE Trans. on Ind. Applicat.

126

View full text Add to dashboard Cite

show abstract

“…Non-stationary signals also originate out of starting current transients in induction motors. Wavelet analysis enables the detection of faults under no-load condition using the transient starting current of the motor [49,50].…”

Section: Advanced Diagnostic Algorithmsmentioning

confidence: 99%

Rotating Electrical Machine Condition Monitoring Automation—A Review

et al. 2017

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Abstract:We review existing machine condition monitoring techniques and industrial automation for plant-wide condition monitoring of rotating electrical machines. Cost and complexity of a condition monitoring system increase with the number of measurements, so extensive condition monitoring is currently mainly restricted to the situations where the consequences of poor availability, yield or quality are so severe that they clearly justify the investment in monitoring. There are challenges to obtaining plant-wide monitoring that includes even small machines and non-critical applications. One of the major inhibiting factors is the ratio of condition monitoring cost to equipment cost, which is crucial to the acceptance of using monitoring to guide maintenance for a large fleet of electrical machinery. Ongoing developments in sensing, communication and computation for industrial automation may greatly extend the set of machines for which extensive monitoring is viable.

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“…As we know that and the relation between (under a certain scaling parameter ) with is described by , we can easily have the (21) below (21) when set the initial analyzing frequency , then the initial . And the converging precision valve is set to be which is rather strict.…”

Section: Detection Of Broken Rotor Barsmentioning

confidence: 99%

“…Some research teams adopted hardware method using a set of hardware filters to capture the CFCFR [19], but it is not only too complicated in circuitry design but very easily subjected to unexpected noise disturbances as well. Wavelet decomposition analysis was presented in [20] and [21] in 1995 to detect broken rotor bars of induction motors, but from the three-dimensional (3-D) figures given by the method, the indicative of the fault is not very obvious, especially when the number of broken rotor bars is very small, for example, one to three broken bars, which shows the defective sensitivity of this method.…”

Section: Introductionmentioning

confidence: 99%

A novel detection method of motor broken rotor bars based on wavelet ridge

Zhang

Ren

Wen-ying

2003

IEEE Trans. On Energy Conversion

113

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Detection of cage motor broken rotor bars has long been an important but difficult job in the detection area of motor faults. The characteristic frequency component of faulted rotor (CFCFR) is very close to the power frequency component but by far less in amplitude, which brings about great difficulty for accurate detection. A new detection method based on wavelet ridge is presented in this paper. Aiming at the motor's starting period during which the motor accelerates progressively and CFCFR approaches the power frequency gradually in frequency spectrum, the wavelet ridge-based method is adopted to analyze this transient procedure and the CFCFR is extracted. The influence of power frequency can be effectively eliminated, and detection accuracy can be greatly improved by using the approach presented in this paper. Also, this is indeed a novel but excellent approach for the detection domain of cage induction motor broken rotor bars.Index Terms-Detection method, motor broken rotor bars, wavelet ridge.

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The application of modern signal processing techniques to rotor fault detection and location within three phase induction motors

Cited by 30 publications

References 1 publication

The Use of the Wavelet Approximation Signal as a Tool for the Diagnosis of Rotor Bar Failures

The Use of the Wavelet Approximation Signal as a Tool for the Diagnosis of Rotor Bar Failures

Rotating Electrical Machine Condition Monitoring Automation—A Review

A novel detection method of motor broken rotor bars based on wavelet ridge

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