A novel approach for broken rotor bar detection in cage induction motors

Milimonfared, J.; Kelk, Homayoun Meshgin; Minassians, Artin Der; Nandi, S.; Toliyat, H.A.

doi:10.1109/ias.1998.732308

Cited by 33 publications

(45 citation statements)

References 6 publications

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“…The diagnostic methods to identify the above faults may involve different types of measurement like coils wound around motors (axial flux or stray flux), vibration, acoustic and motor current signature analyses (MCSA) [1], [2]. Among the various diagnosis techniques of electrical and mechanical faults, deep interest has been paid to the monitoring of current, voltage or stray flux [3], [4], [5] due to their applicability, cost, flexibility, reliability and simplicity. However, according to current and voltage signals, the computation of other machine variables like instantaneous active and reactive powers, torque, rotor speed, negative and positive sequence of current, voltage and impedance, the spatial vectors of voltages and currents and parameter estimation can give also information about operating conditions for diagnosis [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Digital Signal Processing for Induction Machines Diagnosis - A Review

Kia

Henao

Capolino

2007

IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society

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The aim of this paper is to present a wide view of the digital signal processing technique (DSPT) which can help to improve induction machine fault diagnosis. The classical as well as the modern signal processing methods are presented with attention to the performances in frequency, time and timefrequency domain analysis. These methods are applied to different signals coming from the three-phase induction machine sensors under different operating conditions. It is shown that the best diagnostic performances can be obtained when the DSPTs are well adapted to the observed phenomena.

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Section: Introductionmentioning

confidence: 99%

Digital Signal Processing for Induction Machines Diagnosis - A Review

Kia

Henao

Capolino

2007

IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society

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“…This approach provides robust results when the machine operates under a certain level of load and perfect stationary regime, but it has some important drawbacks when it is applied to diagnose the condition of unloaded or light-loaded machines or machines driving variable loads or supplied with fluctuating voltages [3,6]. Some methods based on the study of the transient processes of the machine [7] and, more concretely, of the startup transient [6,[8][9][10][11], have been developed recently. The basis of these last methods is the detection of the left sideband harmonic, associated with the fault, during that transient.…”

Section: Introductionmentioning

confidence: 99%

An Analytical Comparison between DWT and Hilbert-Huang-Based Methods for the Diagnosis of Rotor Asymmetries in Induction Machines

Antonino-Daviu

Riera-Guasp

Roger-Folch

et al. 2007

2007 IEEE Industry Applications Annual Meeting

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In the paper two alternative tools are applied and compared in order to diagnose the presence of rotor asymmetries in induction machines. Both tools are applied to the stator startup current. The objective is to extract the evolution during the startup transient of the left sideband harmonic associated with the asymmetry, which constitutes a reliable evidence of the presence of the fault. The first tool is the Discrete Wavelet Transform (DWT) and its validity for the diagnosis was proven by the authors in previous works, even in cases where the classical Fourier approach does not lead to correct results. Despite its good results, some constraints remained, such as the selection of an optimal mother wavelet or the possible overlap between frequency bands associated with the wavelet signals. In the paper, an alternative time-frequency decomposition tool, the Hilbert-Huang Transform (HHT), is applied to the startup current for detecting the harmonic. This tool might allow avoiding some of the limitations of the DWT, maintaining its reliability. Both approaches are applied to experimental signals obtained under various operation conditions. Finally, the results are analyzed and compared, showing the robustness of both approaches for the diagnosis of rotor asymmetries in induction machines.

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“…Mechanical faults [1][2][3][4][5][6][7]: broken rotor bars bearing damage dynamic eccentricity Electrical Faults [8][9]:…”

Section: Introductionmentioning

confidence: 99%

On-Line System for Fault Detection in Induction Machines based on Wavelet Convolution

Cusidó

Rosero

Cusido

et al. 2007

2007 IEEE Power Electronics Specialists Conference

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Motor Current Signature Analysis has been successfully used in induction machines for fault diagnosis. The method, however, does not always achieve good results when the load torque is not constant. For proper results, temporal analysis on certain frequency harmonics is needed. This paper proposes an automatic system for fault analysis based on wavelet functions, which allows on-line fault detection in the time domain, achieving good experimental results both on stationary and non-stationary states.

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A novel approach for broken rotor bar detection in cage induction motors

Cited by 33 publications

References 6 publications

Digital Signal Processing for Induction Machines Diagnosis - A Review

Digital Signal Processing for Induction Machines Diagnosis - A Review

An Analytical Comparison between DWT and Hilbert-Huang-Based Methods for the Diagnosis of Rotor Asymmetries in Induction Machines

On-Line System for Fault Detection in Induction Machines based on Wavelet Convolution

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