Hybrid Artificial Intelligence based Fault Diagnosis of SVPWM Voltage Source Inverters for Induction Motor

Rajeswaran, N.; Swarupa, M. Lakshmi; Rao, T. Sanjeeva; Chetaswi, K.

doi:10.1016/j.matpr.2017.11.119

Cited by 17 publications

(8 citation statements)

References 12 publications

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“…-AI algorithms have to be used to build a better performance, low cost, continuous, and on-line CM and FD method [79]. -AI hybrid systems should be developed to deal with multiple faults [80].…”

Section: Challenges and Future Trendsmentioning

confidence: 99%

A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

AlShorman

Alshorman

2021

IJECE

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Nowadays, induction motor (IM) is extensively used in industry, including mechanical and electrical applications. However, three main types of IM faults have been discussed in the literature, bearing, stator, and rotor. Importantly, stator and rotor faults represent approximately 50%. Traditional condition monitoring (CM) and fault diagnosis (FD) methods require a high processing cost and much experience knowledge. To tackle this challenge, artificial intelligent (AI) based CM and FD techniques are extensively developed. However, there have been many review research papers for intelligent CM and FD machine learning methods of rolling elements bearings of IM in the literature. Whereas there is a lack in the literature, and there are not many review papers for both stator and rotor intelligent CM and FD. Thus, the proposed study's main contribution is in reviewing the CM and FD of IM, especially for the stator and the rotor, based on AI methods. The paper also provides discussions on the main challenges and possible future works.

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Section: Challenges and Future Trendsmentioning

confidence: 99%

A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

AlShorman

Alshorman

2021

IJECE

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“…Dentre outras complexidades atribuídas ao diagnóstico correto por este método existe também a interferência ou alteração das características dos sinais por: variação dos níveis de carga no eixo da máquina, Mabrouk and Zouzou (2015); alternância de velocidade de operação, Martin-Diaz et al 2017; relação Sinal-Ruído, Singh et al (2015); indução de componentes de frequência próximos ao componente fundamental com sobreposição de sinais característicos, Naha et al (2016) e a modulação e distorção dos sinais de alimentação pelos inversores de frequência atuando no controle da máquina, Rajeswaran et al (2018).…”

Section: Figura 1 Percentual De Falhas Em MI Por Origemunclassified

Harmônicos de Referência Observados por Sistemas Inteligentes na Identificação de Falhas Elétricas em Motores de Indução

Gongora¹,

Silva

Goedtel

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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This article presents a methodology for the classification of electric faults in induction motors through the treatment of search harmonic reference signals injected into the machine's power supply. The proposal is based on the observation by intelligent sistems of the electric current signals of the motor, preprocessed by the FFT and with the analysis of the signature components of the machine. However, the supply voltage signals are multiplexed with harmonic reference signals inserted by the frequency converter module of the machine itself. In order to improve accuracy in fault classification and diagnose degradation status, as well as to attest to the functionality of the method, the practical data of the actual application of the system in the laboratory are processed by different intelligent systems. In this aspect of generalization of the proposal the results are validated by different algorithms whose performances are compared between the following methods: k-nearest neighbors, Naive Bayes, Support Vector Machine, Multilayer Perceptron and Decision tree. Resumo: Este artigo apresenta uma metodologia para a classificação de falhas elétricas em motores de indução através do tratamento de sinais harmônicos de referência de busca injetados pela fonte de alimentação da máquina. A proposta baseia-se na observação por sistemas inteligentes dos sinais de corrente elétrica do motor, pré-processados pela FFT e com a análise das componentes de assinatura da máquina. Para tanto, os sinais de tensão de alimentação são multiplexados com sinais de referência harmônica inseridos pelo próprio módulo inversor de frequência da máquina. Buscando melhorar a precisão na classificação de falhas e diagnosticar o estado de degradação, bem como atestar a funcionalidade do método, dados práticos da aplicação real do sistema em laboratório são processados por sistemas inteligentes distintos. Neste aspecto de generalização da proposta os resultados são validados por diferentes algoritmos cujos desempenhos são comparados entre os seguintes métodos: k-vizinhos mais próximos, Naive Bayes, Máquina de Vetor de Suporte, Perceptron multicamadas eÁrvore de Decisão.

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“…Some of these techniques use expert systems [9], fuzzy logic [10], artificial neural networks (ANNs) [11], Bayesian inference [12], genetic algorithms (GA) [13], and SVM [14], etc. Other combined tools use Fuzzy Logic ANN [15], Recurrent Neural Networks and Dynamic Bayesian networks [16], and Neuro-Genetic Algorithm [17].…”

Section: Introductionmentioning

confidence: 99%

Optimized multi layer perceptron artificial neural network based fault diagnosis of induction motor using vibration signals

Khoualdia¹,

Lakehal²,

Chelli³

et al. 2021

Diagnostyka

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Installations and the detection of their faults has become a major challenge. In order to develop a reliable approach for monitoring and diagnosis faults of these components, a test rig was mounted. In this article, a Multi Layer Perceptron (MLP) Artificial Neural Network (ANN) has been structured and optimized for online monitoring of induction motors. The input layer of our ANN used eight indicators calculated from the collected time signals and which represent the different states of the motor (Healthy, broken rotor bars, bearing fault and Misalignment) and the output layer used a codified matrix. However, based on L27 Taguchi design, the architecture for the hidden layers of our network is chosen, with the use of the Levenberg-Marquardt learning algorithm. Garson's algorithm and connection weight approach showed that there's a great sensitivity of the crest factor, the kurtosis and the variance on the effectiveness of our diagnostic system. Consequently, the obtained results are capable of detecting faults in the induction motor under different operating conditions.

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Hybrid Artificial Intelligence based Fault Diagnosis of SVPWM Voltage Source Inverters for Induction Motor

Cited by 17 publications

References 12 publications

A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

Harmônicos de Referência Observados por Sistemas Inteligentes na Identificação de Falhas Elétricas em Motores de Indução

Optimized multi layer perceptron artificial neural network based fault diagnosis of induction motor using vibration signals

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