A Comparison of External and Internal Methods of Signal Spectral Analysis for Broken Rotor Bars Detection in Induction Motors

Eltabach, Mario; Charara, Ali; Zein, I.

doi:10.1109/tie.2003.822083

Cited by 97 publications

(36 citation statements)

References 10 publications

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“…Similarly, previous contributions have shown that broken bar fault signatures close to the fifth and seventh current harmonics offer valuable broken bar fault detection potential [29], [41]. Therefore, before analyzing the ZSC results, it is crucial to study the MCSA method's capability in detail.…”

Section: B Mcsa Resultsmentioning

confidence: 97%

Comparative experimental investigation of broken bar fault detectability in induction motors

Gyftakis

Antonino-Daviu

Garcia-Hernandez

et al. 2015

2015 IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)

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This article has been accepted for publication in IEEE Transactions on Industry Applications. "© © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. Abstract -It has been shown in the past that the zerosequence current spectrum can be reliably used to detect broken bar faults in induction motors. Previous work was carried out with extensive FEM analysis. Although it allows detailed study of spatial and time-dependent electromagnetic characteristics of induction motors, FEM is a heavily timeconsuming tool and this limits full study. So, in this work, extensive experimental testing has been performed to validate the zero sequence current spectrum for detecting rotor asymmetries. Three identical induction motors have been used: one healthy, one with a broken rotor bar, and one with two broken rotor bars. The motors were tested under different voltage supply levels and with different mechanical loads. The zero-sequence current spectrum was calculated after measuring the three phase currents. It is for the first time experimentally shown that this approach offers greater diagnostic potential than traditional MCSA. CURVE is the Institutional Repository for Coventry University

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Section: B Mcsa Resultsmentioning

confidence: 97%

Comparative experimental investigation of broken bar fault detectability in induction motors

Gyftakis

Antonino-Daviu

Garcia-Hernandez

et al. 2015

2015 IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)

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“…Thus, the results of the torque estimator has the effect of both inputs (currents) and outputs (flux linkages and torque) of a motor which are closer to the physical condition inside a motor than the currents alone as used in. 11,13 C. Rotor Speed Estimator…”

Section: B Air-gap Torque Estimatormentioning

confidence: 99%

“…[1][2]3,4,5,6,7,8,9,10,11,12,13,14 A widely studied method for motor fault detection is frequency spectrum analysis. 4,[6][7]8,9,11,13 In, 5 the effect of broken bars on the air-gap torque was used as a fault indicator. However, manual investigation of the torque profile was needed to classify the motor as healthy or faulty.…”

Section: Introductionmentioning

confidence: 99%

Rotor Bar Fault Monitoring Method Based on Analysis of Air-Gap Torques of Induction Motors

Silva

Povinelli

Demerdash

2013

IEEE Trans. Ind. Inf.

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A robust method to monitor the operating conditions of induction motors is presented. This method utilizes the data analysis of the air-gap torque profile in conjunction with a Bayesian classifier to determine the operating condition of an induction motor as either healthy or faulty. This method is trained offline with datasets generated either from an induction motor modeled by a time-stepping finite-element (TSFE) method or experimental data. This method can effectively monitor the operating conditions of induction motors that are different in frame/class, ratings, or design from the motor used in the training stage. Such differences can include the level of load torque and operating frequency. This is due to a novel air-gap torque normalization method introduced here, which leads to a motor fault classification process independent of these parameters and with no need for prior information about the motor being monitored. The experimental results given in this paper validate the robustness and efficacy of this method. Additionally, this method relies exclusively on data analysis of motor terminal NOT THE PUBLISHED VERSION; this is the author's final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation at the bottom of the page.

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“…In the circle of electrical engineers, the most prominent method is the motor current signal analysis (MCSA), which relies on the processing of stator current with the fast Fourier transform (FFT) and a subsequent analysis of its frequency spectrum [4], [14]. Other techniques that analyses the spectra of signals like torque [15]- [16], speed [17], flux [18]- [20], and electrical power [21]- [22] are also valuable. Different signal processing techniques like the Park's vector approach [23]- [24], Hilbert transform [25], Wavelets [6]- [8], [26], Hilbert-Huang transform [27] and many others, have been applied over the years.…”

Section: Introductionmentioning

confidence: 99%

Reliable detection of broken rotor bars in induction motors via MUSIC and ZSC methods

Moriñigo-Sotelo

Romero‐Troncoso

Antonino-Daviu

et al. 2016

2016 XXII International Conference on Electrical Machines (ICEM)

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A Comparison of External and Internal Methods of Signal Spectral Analysis for Broken Rotor Bars Detection in Induction Motors

Cited by 97 publications

References 10 publications

Comparative experimental investigation of broken bar fault detectability in induction motors

Comparative experimental investigation of broken bar fault detectability in induction motors

Rotor Bar Fault Monitoring Method Based on Analysis of Air-Gap Torques of Induction Motors

Reliable detection of broken rotor bars in induction motors via MUSIC and ZSC methods

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