Instantaneous power spectrum analysis for broken bar fault detection in inverter-fed six-phase squirrel cage induction motor

Maouche, Yassine; Oumaamar, Mohamed El Kamel; Boucherma, M.; Khezzar, A.

doi:10.1016/j.ijepes.2014.04.030

Cited by 27 publications

(14 citation statements)

References 33 publications

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“…This method is based on the extraction of the signal supply stream from the envelope by using the Hilbert transform, the construction of the analytical signal based on the square of the envelope and then performing the spectral analysis of the signal with the FFT. Maouche et al [ 28] also investigated broken bar defects by using FFT analysis of the spectrum of the instantaneous power in the six phases of an inverter-fed motor.…”

Section: Research Articlementioning

confidence: 99%

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Application of intelligent tools to detect and classify broken rotor bars in three‐phase induction motors fed by an inverter

Godoy

Silva

Goedtel³

et al. 2016

IET Electric Power Applications

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A comprehensive study of intelligent tools used to classify broken rotor bars in induction motors, which operate with three different types of frequency inverters, is presented. The diagnosis of defective rotor bars is a critical issue for the predictive maintenance of induction motors. A proper classification of these defects in their early stages of evolution is necessary for preventing major machine failures and production downtime. The proposed approach is performed by analysing the amplitude of the stator current signal in the time domain, using a dynamic acquisition rate based on machine frequency supply. To assess classification accuracy under the various severity levels of the faults, the performance of four different learning machine techniques is investigated: (i) fuzzy ARTMAP network; (ii) support vector machine (sequential minimal optimisation); (iii) k-nearest neighbour; and (iv) multilayer perceptron network. Results obtained from 1274 experimental tests are presented in order to validate the study, which considers a wide range of load conditions and operating frequencies. Experimental results presented in this study validate the robustness and efficacy of the proposed approach.

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Section: Research Articlementioning

confidence: 99%

“…To reproduce these defects a procedure of drilling the bars that compose the rotor cage was used. The procedure is carried out using a drill press whose bit is sufficiently larger than the bar as per [21,28,37,38]. This operation results in a set of rotors for the test bench.…”

Section: Proposed Approach For Fault Diagnosis and Classificationmentioning

confidence: 99%

Application of intelligent tools to detect and classify broken rotor bars in three‐phase induction motors fed by an inverter

Godoy

Silva

Goedtel³

et al. 2016

IET Electric Power Applications

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“…Due to the availability of stator voltage and current measurements together with the minimum influences of noise compared to MCSA (Trzynadlowski, A.M., 1999) a new domain was opened combining these components as instantaneous partial and total power (Samaga and Vittal, 2012;Maouche et al, 2014;Kucuker and Bayrak, 2013), active and reactive power (Akagi et al, 1984;Olivier et al, 2009;Drif and Cardoso, 2009;Intesar et al, 2010;Drif and Cardoso, 2012), complex apparent power (Bitoleanu et al, 2007;Fiorucci, 2015), and mechanical power transferred from rotor to stator (Boudebbouz et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Simulation of different power methods for induction generator faults detection and diagnosis

Leksir

Bensaker

2019

IJPEC

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This paper deals with a simulation of different power methods to detect and diagnose induction generator faults. Instantaneous partial and total power, active and reactive power, complex apparent power and transformed power from mechanic to electric nature are revisited, simulated and discussed in this paper for induction generator rotor broken bars and stator short cuts faults detection and diagnosis. Fast Fourier transform (FFT) and PQ transform algorithms are used as comparison tools. Simulation results show that, on one hand, active, reactive and complex apparent power can only be used to detect evolution of rotor faults. On the other hand, partial, total and power transferred from mechanical to electrical nature are able to detect induction generator faults evolution with the advantage of eliminating electrical distortions and influence of low quality of supplying voltage. Furthermore, the implementation of the PQ transformation offers the possibility to isolate load influence from rotor faults and stator ones.

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“…A contribuição do método refere-se à extração da envoltória do sinal de corrente de alimentação utilizando a transformada Hilbert, construção do sinal analítico com base no quadrado da envoltória e então executar a análise do espectro do sinal com a FFT. Ainda, o estudo de Maouche et al (2014) investiga as falhas de barras quebradas por meio da análise FFT dos espectros das potências instantâneas nos seis terminais de um motor alimentado por inversor de frequência.…”

Section: Falhas De Rotorunclassified

“…Além disso, este trabalho considera o emprego de 5 modelos de inversores de frequência, operando com frequência de chaveamentos distintas, enquanto outros estudos disponíveis na literatura pesquisada empregam a utilização de um único inversor, com exceção do trabalhos de Cristaldi et al (2009), Maouche et al (2014, que consideram o uso de 2 inversores.…”

Section: Considerações Finaisunclassified

Multiclassificador inteligente de falhas no domínio do tempo em motores de indução trifásicos alimentados por inversores de frequência

Godoy¹

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Instantaneous power spectrum analysis for broken bar fault detection in inverter-fed six-phase squirrel cage induction motor

Cited by 27 publications

References 33 publications

Application of intelligent tools to detect and classify broken rotor bars in three‐phase induction motors fed by an inverter

Application of intelligent tools to detect and classify broken rotor bars in three‐phase induction motors fed by an inverter

Simulation of different power methods for induction generator faults detection and diagnosis

Multiclassificador inteligente de falhas no domínio do tempo em motores de indução trifásicos alimentados por inversores de frequência

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