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Prediction of Motor Failure Time Using An Artificial Neural Network.
Scalabrini Sampaio, Gustavo; Vallim Filho, Arnaldo Rabello de Aguiar; Santos da Silva, Leilton; Augusto da Silva, Leandro.
Afiliação
  • Scalabrini Sampaio G; Postgraduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, Rua da Consolação, 896, Prédio 30-Consolação, São Paulo 01302-907, Brazil. gustavo.sampaio@mackenzista.com.br.
  • Vallim Filho ARA; Computer Science Dept., Mackenzie Presbyterian University, Rua da Consolação, 896, Prédio 31-Consolação, São Paulo 01302-907, Brazil. arnaldo.aguiar@mackenzie.br.
  • Santos da Silva L; EMAE-Metropolitan Company of Water & Energy, Avenida Nossa Senhora do Sabará, 5312-Vila Emir, São Paulo 04447-902, Brazil. leilton@emae.com.br.
  • Augusto da Silva L; Postgraduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, Rua da Consolação, 896, Prédio 30-Consolação, São Paulo 01302-907, Brazil. leandroaugusto.silva@mackenzie.br.
Sensors (Basel) ; 19(19)2019 Oct 08.
Article em En | MEDLINE | ID: mdl-31597304
Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Brasil País de publicação: Suíça

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Brasil País de publicação: Suíça