Broken rotor bar fault diagnosis using fast Fourier transform applied to field-oriented control induction machine: simulation and experimental study

Ameid, Tarek; Menacer, Arezki; Talhaoui, Hicham; Harzelli, Imadeddine

doi:10.1007/s00170-017-0143-2

Cited by 47 publications

(36 citation statements)

References 20 publications

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“…The number of TDSF of Winding A, Winding B and sound is set to 10 [35]. For FFT, the sample frequency is set to 8000, the sample points are the same as the time domain which is set as 1000 [16]. For STFT, the window function is set as a Hamming window [17].…”

Section: A Results and Discussion Of Different Feature Extraction Metmentioning

confidence: 99%

“…Fast Fourier Transform (FFT) is a commonly used in signal processing [15]. However, it is not suitable for non-stationary signals which require a time-frequency analysis [16]. Short Time Fourier Transform (STFT) may be a viable alternative but it has defects in the resolution of time and frequency, making it impossible to resolve both time and frequency at the same time [17].…”

Section: Introductionmentioning

confidence: 99%

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A Fault Diagnostic Method for Induction Motors Based on Feature Incremental Broad Learning and Singular Value Decomposition

2019

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The occurrence of fault in induction motors is dangerous in our daily life. It is significant to diagnose motor component faults accurately and quickly. In this paper, we propose an efficient and responsive motor fault diagnostic method based on Feature Incremental Broad Learning (FIBL) and Singular Value Decomposition (SVD). Firstly, we extract fault features from raw signals with Particle Swarm Optimization-Variation Model Decomposition, Sample Entropy and Time Domain Statistical Features. Secondly, these features are input into a broad learning system to train a network. Then we use FIBL to retrain the network if the diagnosis accuracy is unsatisfactory. Finally, SVD is used to further simplify the system structure to reduce diagnostic errors. In order to evaluate the performance of the diagnostic system, experiments are conducted. Experimental results show that with the proposed diagnostic method, motor component faults detection is quicker and more accurate. INDEX TERMS Fault diagnosis, feature extraction, incremental broad learning, singular value decomposition, induction motor.

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Section: A Results and Discussion Of Different Feature Extraction Metmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Fault Diagnostic Method for Induction Motors Based on Feature Incremental Broad Learning and Singular Value Decomposition

2019

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“…based on electrical monitoring such as stator current monitoring [6], axial flux, instantaneous power [7], active reactive power, air-gap torque monitoring and current Park components [8] followed by thermal and vibration monitoring [9]. Several authors applied modern computing approaches such as genetic algorithm, wavelet analysis, expert systems, Neural Networks [10,11], and Fuzzy Logic [12].…”

Section: Several Methods Have Been Developed For the Identification Omentioning

confidence: 99%

“…The most important aspect of field-oriented control applied to IM is the decoupling of stator current into two components, used for generating magnetizing flux and torque [1]. The decoupling allows us to control the IM as a simple DC machine with separate excitation [6]. The…”

Section: Indirect Field Oriented Controlmentioning

confidence: 99%

Fault Eccentricity Diagnosis in Variable Speed Induction Motor Drive Using DWT

Reda¹,

fayçal²,

Bahi³

2017

AMA_C

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This paper describes the monitoring and diagnosis of eccentricity fault in variable speed induction motor drive. The used control technique is the indirect field oriented control (IFOC) fuzzy logic based controller to ensure the robust speed regulation and to compensate the fault effects. The variable speed induction motor can be affected by various kinds of faults. Airgap eccentricity is one of the major defects occurring in such electric drives; its detection could be useful for preventing potential catastrophic failures. A dynamic model taking into account the faults is proposed based on the approach of magnetically coupled coils to simulate the behavior of eccentricity faults in induction motor. This work presents two approaches for diagnosis and detection of eccentricity faults and evaluation of their severity based on monitoring of the stator current signals, using Park vector method and discrete wavelet transform (DWT) with different approaches to distinguish healthy as well as faulty conditions of the machine. The obtained simulation results via the proposed technique allow detection and diagnosis of eccentricity fault and identify their severity.

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“…Это вызвано тем, что потери от внезапной остановки технологического оборудования многократно превышают потери от плановой замены электродвигателя в его составе. В связи с этим интерес специалистов и ученых к вопросам эксплуатационной диагностики в настоящее время высок [1,2].…”

Section: Introductionunclassified

The Development of Topological Diagnostic Methods of Asynchronous Electric Machines

Курилин¹,

Denisov²

2018

DREAM

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The article develops scientific foundations for the topological diagnostic methods of asynchronous electric machines (AEM) towards the development of practical applications. The topological diagnostics is based on testing the parametric characteristics of the AEM vector space or its separate subspaces. Methods for testing separate subspaces of the vector space and methods for testing AEM stators and rotors are discussed; some specialized diagnostic factors are illustrated by calculation examples. The problems stated are solved by linear algebra methods.

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Broken rotor bar fault diagnosis using fast Fourier transform applied to field-oriented control induction machine: simulation and experimental study

Cited by 47 publications

References 20 publications

A Fault Diagnostic Method for Induction Motors Based on Feature Incremental Broad Learning and Singular Value Decomposition

A Fault Diagnostic Method for Induction Motors Based on Feature Incremental Broad Learning and Singular Value Decomposition

Fault Eccentricity Diagnosis in Variable Speed Induction Motor Drive Using DWT

The Development of Topological Diagnostic Methods of Asynchronous Electric Machines

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