Diagnóstico de defeitos em estatores de motores de indução trifásicos por meio de sensores piezelétricos de baixo custo e redes neurais artificiais
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Data
2017-09-29
Autores
Silva Filho, Nelson Medeiros da [UNESP]
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Editor
Universidade Estadual Paulista (Unesp)
Resumo
O Motor de Indução Trifásico (MIT) é o mais utilizado em aplicações industriais e acionamentos eletromecânicos diversos, por ser um motor de construção robusta, ter baixo custo de fabricação e bom rendimento elétrico. Nesse cenário, destaca-se a importância de um correto diagnóstico de falhas de uma forma incipiente, evitando perdas nos processos produtivos e danos severos nessas máquinas. A estratégia da manutenção corretiva impõe, nos processos produtivos, paradas inesperadas da linha de produção podendo causar grandes prejuízos financeiros. Nesse contexto, a manutenção preditiva, auxiliada por sistemas de monitoramento constante, vem de encontro as necessidades dos processos industriais pois, além de prever paradas inesperadas, permite um planejamento da produção mais seguro, no que tange a continuidade do processo e administração da equipe de manutenção especializada. Entretanto, requer monitoramento constante com uma análise dos dados e conhecimento prévio do sistema para a avaliação dos parâmetros e das variáveis envolvidas. Esse trabalho de pesquisa propôs o desenvolvimento de uma abordagem para diagnosticar defeitos em MIT, por meio de diafragmas piezelétricos de baixo custo e sistemas inteligentes do tipo Redes Neurais Artificiais (RNA). Mais especificamente, defeitos elétricos do tipo curto-circuito entre espiras de uma mesma bobina nos estatores dessas máquinas. Para tal, foi implementada uma bancada experimental em ambiente de laboratório, onde foram realizados ensaios com simulações de falhas relacionadas a curto-circuito entre espiras das bobinas do enrolamento do estator, visando a obtenção de dados de corrente elétrica e vibração do MIT, para o treinamento das arquiteturas de RNA. Em seguida essas redes foram treinadas e validadas, e a análise dos resultados obtidos foi realizada através de critérios estatísticos como taxa de acerto percentual, erro relativo percentual, desvio padrão e coeficiente de correlação entre os dados. Os resultados indicam que a abordagem proposta mostrou-se precisa e robusta, sinalizando a factibilidade do uso da técnica em motores de maior potência e tensão elétrica de operação, que possuem valores de custo para aquisição relativamente altos.
The three-phase induction motor (MIT) is the most widely used in industrial applications and various electromechanical drives, as it is a robust construction engine, low manufacturing cost and good electrical performance. In this scenario, the importance of a correct fault diagnosis in an incipient way is highlighted, avoiding losses in the productive processes and severe damages in these machines. The strategy of corrective maintenance imposes, in the productive processes, unexpected stops of the production line and can cause great financial losses. In this context, the predictive maintenance, aided by constant monitoring systems, meets the needs of industrial processes because, in addition to anticipating unexpected stops, it allows a safer production planning regarding the continuity of the process and administration of the maintenance team specialized. However, it requires constant monitoring with an analysis of the data and prior knowledge of the system for the evaluation of the parameters and variables involved. This research project proposed the development of an approach to diagnose defects in MIT, through low cost piezoelectric diaphragms and intelligent artificial neural networks (ANNs). More specifically, short-circuit electrical defects between turns of the same coil in the stators of these machines. For this, an experimental bench was implemented in a laboratory environment, where tests were performed with fault simulations related to short circuits between turns of the coils of the stator winding, aiming to obtain data of electrical current and vibration of the MIT for the training of ANN architectures. Afterwards, these networks were trained and validated, and the analysis of the results obtained was performed through statistical criteria such as percentage success rate, relative percentage error, standard deviation and correlation coefficient between the data. The results indicated that the proposed approach proved to be accurate and robust, signaling the feasibility of using the technique in engines with higher power and operating voltage, which have relatively high acquisition cost values.
The three-phase induction motor (MIT) is the most widely used in industrial applications and various electromechanical drives, as it is a robust construction engine, low manufacturing cost and good electrical performance. In this scenario, the importance of a correct fault diagnosis in an incipient way is highlighted, avoiding losses in the productive processes and severe damages in these machines. The strategy of corrective maintenance imposes, in the productive processes, unexpected stops of the production line and can cause great financial losses. In this context, the predictive maintenance, aided by constant monitoring systems, meets the needs of industrial processes because, in addition to anticipating unexpected stops, it allows a safer production planning regarding the continuity of the process and administration of the maintenance team specialized. However, it requires constant monitoring with an analysis of the data and prior knowledge of the system for the evaluation of the parameters and variables involved. This research project proposed the development of an approach to diagnose defects in MIT, through low cost piezoelectric diaphragms and intelligent artificial neural networks (ANNs). More specifically, short-circuit electrical defects between turns of the same coil in the stators of these machines. For this, an experimental bench was implemented in a laboratory environment, where tests were performed with fault simulations related to short circuits between turns of the coils of the stator winding, aiming to obtain data of electrical current and vibration of the MIT for the training of ANN architectures. Afterwards, these networks were trained and validated, and the analysis of the results obtained was performed through statistical criteria such as percentage success rate, relative percentage error, standard deviation and correlation coefficient between the data. The results indicated that the proposed approach proved to be accurate and robust, signaling the feasibility of using the technique in engines with higher power and operating voltage, which have relatively high acquisition cost values.
Descrição
Palavras-chave
Motor de indução trifásico, Diagnóstico de falhas, Redes neurais artificiais, Diafragmas piezelétricos de baixo custo, Three phase induction motor, Troubleshooting, Artificial neural networks, Low cost piezoelectric diagrams