Detection of Rotor Eccentricity Faults in a Closed-Loop Drive-Connected Induction Motor Using an Artificial Neural Network

Huang, Xianghui; Habetler, T.G.; Harley, R.G.

doi:10.1109/tpel.2007.900607

Cited by 91 publications

(28 citation statements)

References 13 publications

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“…As mentioned above, every wavelet function could be considered as a filter on the frequency domain [10]. By tuning the adequate wavelet function to the operation point (i.e., for the expected frequency) specific fault harmonic detection is thus possible.…”

Section: Fault Detection By Means Of Wavelet-based Convolutionmentioning

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: Fault Detection By Means Of Wavelet-based Convolutionmentioning

confidence: 99%

“…Moreover, the current spectrum is influenced by many factors, including electric supply, noise, motor geometry, and fault condition. Besides, the FFT analysis is not valid in the case of fault frequency shifts due to variable torque or non-constant speeds [10].…”

Section: Introductionmentioning

confidence: 99%

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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“…Faiz et al [11] employed instantaneous power harmonics to detect mixed IM eccentricity defect. Huang et al [12] applied an artificial neural network for the detection of rotor eccentricity faults. Esfahani et al [13] utilized the Hilbert-Huang transform to detect IM eccentricity fault.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Harmonic Synthesis Technique for Air-Gap Eccentricity Fault Diagnosis in Induction Motors

Wang

Ismail

2017

Chin. J. Mech. Eng.

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Induction motors (IMs) are commonly used in various industrial applications. To improve energy consumption efficiency, a reliable IM health condition monitoring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is proposed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are synthesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM airgap eccentricity diagnosis. The effectiveness of the proposed harmonic synthesis technique is examined experimentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.

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“…Recently, both current and voltage signals of a motor are used to detect rotating machinery faults. The research detects induction motor faults by using current signals [7]. The research uses generator output current to monitor generator condition [8].…”

Section: Introductionmentioning

confidence: 99%

Detection of Gearbox lubrication Using PSO-Based WKNN

Lee¹,

Kuo²,

Liu³

et al. 2013

Measurement Science Review

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This paper proposes an optimization classification model, which combines particle swarm optimization (PSO) with weighted knearest neighbors (WKNN), namely PWKNN. The model optimizes the weight and k parameter of WKNN to improve the detection accuracy of gearbox lubrication levels. In the experiment, the current signals of the generator are measured, and the relative frequency spectrum of the measured signals is illustrated by using fast Fourier transform (FFT). The features from the spectrum are extracted, and then the optimal weight and k parameter of WKNN are obtained by using PSO. The average detection accuracy of gearbox lubrication levels is 96% by using PWKNN, which the result shows that the proposed PWKNN can efficiently detect the lubrication level of gearboxes. The experiment also shows that the performance of the proposed PWKNN by using the current signals of the generator is superior to that by using typical vibration signals of a gearbox. In addition, the accuracy can reach 95.4% even in environments with 20 dB noise interference.

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Detection of Rotor Eccentricity Faults in a Closed-Loop Drive-Connected Induction Motor Using an Artificial Neural Network

Cited by 91 publications

References 13 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

An Intelligent Harmonic Synthesis Technique for Air-Gap Eccentricity Fault Diagnosis in Induction Motors

Detection of Gearbox lubrication Using PSO-Based WKNN

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