An application of artificial neural networks and PCA for stator fault diagnosis in inverter-fed induction motors

Godoy, Wagner Fontes; Silva, Ivan N. da; Goedtel, Alessandro; Palácios, Rodrigo Henrique Cunha; Bazan, Gustavo Henrique; Moriñigo-Sotelo, Daniel

doi:10.1109/icelmach.2016.7732822

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…Results showed that the proposed method could effectively identify multiple faults. An FD/D method for stator short-circuit faults in an inverter-fed IM was presented by Godoy, da Silva, Goedtel, Palácios, Bazan and Morinigo-Sotelo. (2016a).…”

Section: Fd/d Based Onmentioning

confidence: 99%

Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey

Drakaki

Karnavas

Tziafettas

et al. 2022

JIEM

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Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research.Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithmsFindings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated.Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015.

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Section: Fd/d Based Onmentioning

confidence: 99%

Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey

Drakaki

Karnavas

Tziafettas

et al. 2022

JIEM

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“…Desta forma, tem-se a distribuição homogênea do fluxo magnético na peça. A quebra de barras do rotor pode ser provocada por vibração, desalinhamento, excentricidade, carga excessiva no eixo do motor [6,7].…”

Section: Introductionunclassified

Neural Classification of Rotor Faults in Three-Phase Induction Motors using Electric Current Signals in the Frequency Domain

Palácios

Silva

Godoy

2023

RITA

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Three-phase induction motors are widely used in different applications in the industry due to their robustness, low cost, and reliability. Untimely identification and correct diagnosis of incipient faults reduce cost and improve the maintenance management of these machines. This paper explores a new method for robust classification of rotor failures in three-phase induction motors (MITs) connected directly to the electrical network, operating in a steady-state, under unbalanced voltages and load conditions. Through an innovative methodology, an analysis of the electrical current signals from 1 hp and 2 hp motors in the frequency domain was performed. Such analysis was applied in constructing input matrices for a Multilayer Perceptron Neural Network (MLPNN) to detect faults. Furthermore, this methodology proved to be robust because the samples of the failing and healthy motors include voltage unbalance conditions in the electrical supply and a significant variation in the load applied to the motor shaft. Such load variation was used for the detection of failures of 1, 2, and 4 broken bars consecutively on the rotor and in the condition of 2 broken bars and 2 other broken bars diametrically opposite. The results were promising and were obtained using 847 real samples from an experimental bench used to construct the neural model and its respective validation.

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“…A falha de excentricidade está associada ao desvio da posição do eixo em relação ao seu referencial axial, este tipo de falha pode acarretar variações dimensionais no entreferro, sendo capaz de impactar de maneira severa na distribuição espacial de fluxo magnético entre o rotor e o estator. A torção do eixo pode ocorrer em decorrência de defeitos no acoplamento mecânico entre a máquina e a carga, bem como consequência de condições de conjugado resistente superiores aos valores suportados pela máquina (GODOY et al, 2016;GONGORA et al, 2016;BIM, 2018).…”

Section: Falhas Em Motores De Indução Trifásicosunclassified

“…Para a reprodução das condições operacionais do MIT, utilizou-se na etapa de aquisição de dados a estrutura disponível no Laboratório de Sistemas Inteligentes (LSI) do Centro Integrado de Pesquisa em Controle e Automação (CIPECA) da Universidade Tecnológica Federal do Paraná Campus Cornélio Procópio (UTFPR-CP). Este arranjo possibilita a medição e análise de grandezas elétricas e mecânicas de MIT sujeitos à estudos, sendo aplicada em diversas pesquisas do ramo que empregam medidas de corrente elétrica de linha, velocidade de rotação do eixo, aplicação de variações de conjugado resistente, desequilíbrio de tensão e análise vibratória (GOEDTEL, 2007;BRONIERA et al, 2013;GONGORA, 2013;COSTA et al, 2015;GONGORA et al, 2016;GODOY et al, 2016;BAZAN et al, 2016BAZAN et al, , 2017BAZAN et al, , 2019BAZAN, 2020;BAZAN et al, 2021;GUEDES et al, 2018GUEDES et al, , 2019BAZAN et al, 2022).…”

Section: Aquisição E Organização De Dadosunclassified

Diagnóstico de falhas de estator em motores de indução trifásicos via informação mútua e transformação de Clarke.

Nunes

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Agradeço a Deus, por colocar pessoas incríveis em meu caminho e realizar grandes obras na minha vida.Agradeço à minha mãe, por sua fé em mim. Privou-se de muitas coisas para que minha formação se tornasse realidade, e sempre esteve comigo, fosse em tristeza ou felicidade.À minha querida Cremilda, que cobriu desde a minha infância as lacunas deixadas pela perda de meu pai, trazendo a essência mais verdadeira do significado de família que hoje tenho como base.Agradeço com especial carinho à minha companheira Emy, por todo o suporte prestado durante todo o tempo, cuja compreensão e o companheirismo ajudaram a superar os momentos mais difíceis.Ao meu irmão e minhas tias, que acreditam no meu sonho e em meu potencial. Eles foram capazes de preencher minhas lacunas e me tornaram mais forte.Agradeço ao meu orientador Prof. Dr. Bruno Augusto Angélico, pela oportunidade, ensinamentos e orientação.Ao meu coorientador Prof. Dr. Alessandro Goedtel, por todos os ensinamentos, com ênfase àqueles que não são passados em sala de aula, como as virtudes da retidão.

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An application of artificial neural networks and PCA for stator fault diagnosis in inverter-fed induction motors

Cited by 9 publications

References 30 publications

Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey

Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey

Neural Classification of Rotor Faults in Three-Phase Induction Motors using Electric Current Signals in the Frequency Domain

Diagnóstico de falhas de estator em motores de indução trifásicos via informação mútua e transformação de Clarke.

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