New methodology to faults detection in induction motors via MCSA

Castelli, Marcelo; Fossati, Juan Pablo; Andrade, Mauro Figueiredo Carvalho de

doi:10.1109/tdc-la.2008.4641817

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…The obtained frequency value had spectrum more than the reference frequency. The detection program was designed to calculate the frequency value indicating rotor fault frequency corresponding to (1). The program would select the data that matched with the determined frequency value.…”

Section: Rotor Fault Detectionmentioning

confidence: 99%

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Design of Motor Current Signature Analyses (MCSA) Based on LabVIEW for Online Detection of Induction Motor Faults

Asfani

Soedibyo

Negara

et al. 2017

JAREE

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Induction motor is the most common used electric motor in industry. Along with its operation, induction motor can savere certain damage which is generally dominated by internal fault, such as s rotor fault, stator fault, and bearing fault. In this paper, the online detection based on motor current signature analyses (MCSA) for such faults was designed using LabVIEW. MCSA was performed to obtain the frequency which indicated the presence of certain damage of induction motor. The experimental result showed that the accuracy of proposed method reached 90 %.

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Section: Rotor Fault Detectionmentioning

confidence: 99%

“…There are many kinds of induction motor damage. However, as stated in [1], about 90 % of induction motor damage cases is dominated by the internal faults. This internal fault can be a broken rotor, stator fault, and bearing fault.…”

Section: Introductionmentioning

confidence: 95%

Design of Motor Current Signature Analyses (MCSA) Based on LabVIEW for Online Detection of Induction Motor Faults

Asfani

Soedibyo

Negara

et al. 2017

JAREE

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“…Várias técnicas para a detecção preditiva de falhas em motores elétricos têm sido bastante difundidas e consolidadas nos últimos anos, a maioria delas utiliza a análise do espectro de vibração ou/e a Análise da Assinatura da Corrente do Motor (Motor Current Signature Analysis -MCSA) (Castelli et al, 2008;Krishna and Kishan, 2013). A MCSA é capaz de verificar assimetrias ou falhas do motor pela análise espectral da corrente do estator, utilizando como ferramenta matemática a Transformada Rápida de Fourier (FFT).…”

Section: Introductionunclassified

Detecção de desequilíbrio de tensão e de desalinhamento no motor de indução através da análise do fluxo de dispersão

Araújo

Filho

Baccarini

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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Os motores de indução trifásicos são largamente empregados no setor industrial devido sua versatilidade, robustez e baixo custo. No entanto, estas máquinas estão sujeitas a diversos tipos de falhas que comprometem o seu funcionamento e a integridade de seus componentes. Dentre essas falhas estão as de desalinhamento e as de desequilíbrio de tensão. Se não detectadas podem causar danos irreversíveis ao motor e perdas no processo industrial. Assim, a importância da confiabilidade e da disponibilidade exigidas para os motores elétricos crescem constantemente. Neste cenário, o presente trabalho apresenta a Análise da Assinatura de Fluxo de Dispersão (SFSA) para detecção de falha de desalinhamento e desequilíbrio de tensão no motor de indução trifásico comparando-a com uma técnica tradicional, Análise da Assinatura de Corrente do Motor (MCSA). Testes experimentais no Laboratório de Máquinas e Transformadores (LAMET) da Universidade Federal de São João del-Rei (UFSJ) foram usados para validar a eficácia dessa ferramenta. Os resultados obtidos mostraram a análise dos sinais de fluxo como uma confiável e simples alternativa para detecção de falha de desalinhamento e de desequilíbrio de tensão, evitando testes invasivos e paradas inesperadas para manutenção da máquina.

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Advanced Signal Processing Methods for Condition Monitoring

Jaroš

Byrtus

Dohnal

et al. 2022

Arch Computat Methods Eng

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Condition monitoring of induction motors (IM) among with the predictive maintenance concept are currently among the most promising research topics of manufacturing industry. Production efficiency is an important parameter of every manufacturing plant since it directly influences the final price of products. This research article presents a comprehensive overview of conditional monitoring techniques, along with classification techniques and advanced signal processing techniques. Compared methods are either based on measurement of electrical quantities or nonelectrical quantities that are processed by advanced signal processing techniques. This article briefly compares individual techniques and summarize results achieved by different research teams. Our own testbed is briefly introduced in the discussion section along with plans for future dataset creation. According to the comparison, Wavelet Transform (WT) along with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA) and Park’s Vector Approach (PVA) provides the most interesting results for real deployment and could be used for future experiments.

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New methodology to faults detection in induction motors via MCSA

Cited by 6 publications

References 7 publications

Design of Motor Current Signature Analyses (MCSA) Based on LabVIEW for Online Detection of Induction Motor Faults

Design of Motor Current Signature Analyses (MCSA) Based on LabVIEW for Online Detection of Induction Motor Faults

Detecção de desequilíbrio de tensão e de desalinhamento no motor de indução através da análise do fluxo de dispersão

Advanced Signal Processing Methods for Condition Monitoring

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