Fault detection and diagnosis of an induction motor using fuzzy logic

Mini, V. P.; Setty, Sivakotaiah.; Ushakumari, S.

doi:10.1109/sibircon.2010.5555123

Cited by 24 publications

(10 citation statements)

References 5 publications

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“…As the computational power of computers is increasing day by day, the researchers are focusing on the implementation of advanced fault diagnostic techniques. These techniques may contain some artificial intelligence-based algorithms, such as neural networks [41], [42] and Fuzzy Logic [43], etc., or some analytical algorithms, such as the finite element analysis [44]- [47] and the inverse problem theory [48]. Unlike conventional forward model-based fault diagnostic techniques, these advances algorithms can lead to more precise and accurate results, but at the same time they require more sophisticated hardware for implementation.…”

Section: Advanced Techniquesmentioning

confidence: 99%

Review of Electrical Machine Diagnostic Methods Applicability in the Perspective of Industry 4.0

Asad

Rassõlkin

Kallaste

et al. 2018

Electrical, Control and Communication Engineering

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Digitalization of the industrial sector and Industry 4.0 have opened new horizons in many technical fields, including electrical machine diagnostics and operation, as well as machine condition monitoring. This paper addresses a selection of electrical machine diagnostics methods that are applicable for the use in the perspective of Industry 4.0, to be used in hand with cloud environments and the possibilities granted by the Internet of Things. The need for further research and development in the field is pointed out. Some potentially applicable future approaches are presented.

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Section: Advanced Techniquesmentioning

confidence: 99%

Review of Electrical Machine Diagnostic Methods Applicability in the Perspective of Industry 4.0

Asad

Rassõlkin

Kallaste

et al. 2018

Electrical, Control and Communication Engineering

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show abstract

“…Dentre as metodologias da inteligência artificial, a lógica fuzzyé bem utilizada em sistemas de monitoramento de falhas no enrolamento do estator (Rodriguez and Arkkio, 2008;Zidani et al, 2003;Mini et al, 2010;Kumar et al, 2010;Karakose et al, 2010). A lógica fuzzy surgiu como um meio de representar a imprecisão inerente ao processo por meio de termos lingüísticos, assim como uma maneira de expressar o conhecimento humano por uma forma mais natural (Zadeh, 1965).…”

Section: Falhas No Estatorunclassified

Sobre a aplicação de sistemas inteligentes para diagnóstico de falhas em máquinas de indução - uma visão geral

Santos

Silva

Suetake

2012

Sba Controle & Automação

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Muitas pesquisas estão sendo desenvolvidas buscando nos sistemas inteligentes soluções para diagnosticar falhas em máquinas elétricas. Estas falhas envolvem desde problemas elétricos, como curto-circuito numa das fases do estator, ate problemas mecânicos, como danos nos rolamentos. Dentre os sistemas inteligentes aplicados nesta área, destacam-se as redes neurais artificiais, os sistemas fuzzy, os algoritmos genéticos e os sistemas híbridos, como o neuro-fuzzy. Assim, o objetivo deste artigo é traçar um panorama geral sobre os trabalhos mais relevantes que se beneficiaram dos sistemas inteligentes nas diferentes etapas de análise e diagnóstico de falhas em motores elétricos, cuja principal contribuição está em disponibilizar diversos aspectos técnicos a fim de direcionar futuros trabalhos nesta área de aplicação.

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“…Different faults may happen in BLDC motors which may be due to electronic or electromechanical defects. They include bearing, stator, dynamic and static imbalances or damaged permanent magnet [1,3,4]. The mentioned faults and the corresponding fault detection (FD) and compensation methods have been thoroughly investigated in the literature.…”

Section: Introductionmentioning

confidence: 99%

Stator fault diagnosis of a BLDC motor based on discrete wavelet analysis using ADAMS simulation

Hosseini

Abedi

2019

SN Appl. Sci.

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In this paper, a method is considered to observe the stator inter-turn fault (SITF) in brushless DC motors (BLDCM). This is a crucial subject to deal with, since the fault may cause expensive replacement of parts in case of late diagnosis. The approach used here is the discrete wavelet transform, which is one of the many kinds of time-frequency analysis approaches. Taking advantage of a model closely related to a real BLDCM, the stator current is simulated and the aforesaid signal-based method is applied to it. The feature used as a parameter to determine whether SITF has occurred or not is the average change or deviation in the energy amount of four signals named high frequency (detail) signals. In other words, first, the difference percentage of the energy parameters between those four signals in healthy and faulty operation mode would be measured. Then, the average percentage of the energy variation amounts are to be compared with a threshold to determine the fault occurrence. Having a precise Simulink/Matlab plus ADAMS model of the motor, the designed algorithm will be validated.

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Fault detection and diagnosis of an induction motor using fuzzy logic

Cited by 24 publications

References 5 publications

Review of Electrical Machine Diagnostic Methods Applicability in the Perspective of Industry 4.0

Review of Electrical Machine Diagnostic Methods Applicability in the Perspective of Industry 4.0

Sobre a aplicação de sistemas inteligentes para diagnóstico de falhas em máquinas de indução - uma visão geral

Stator fault diagnosis of a BLDC motor based on discrete wavelet analysis using ADAMS simulation

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