Prediction of Motor Failure Time Using An Artificial Neural Network
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 wer...
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MDPI AG
2019-10-01
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Series: | Sensors |
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Online Access: | https://www.mdpi.com/1424-8220/19/19/4342 |
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author | Gustavo Scalabrini Sampaio Arnaldo Rabello de Aguiar Vallim Filho Leilton Santos da Silva Leandro Augusto da Silva |
author_facet | Gustavo Scalabrini Sampaio Arnaldo Rabello de Aguiar Vallim Filho Leilton Santos da Silva Leandro Augusto da Silva |
author_sort | Gustavo Scalabrini Sampaio |
collection | DOAJ |
description | 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, <i>k</i>-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. |
first_indexed | 2024-04-11T13:59:00Z |
format | Article |
id | doaj.art-2a36b07ad77c42d5b2771e3523c06c33 |
institution | Directory Open Access Journal |
issn | 1424-8220 |
language | English |
last_indexed | 2024-04-11T13:59:00Z |
publishDate | 2019-10-01 |
publisher | MDPI AG |
record_format | Article |
series | Sensors |
spelling | doaj.art-2a36b07ad77c42d5b2771e3523c06c332022-12-22T04:20:11ZengMDPI AGSensors1424-82202019-10-011919434210.3390/s19194342s19194342Prediction of Motor Failure Time Using An Artificial Neural NetworkGustavo Scalabrini Sampaio0Arnaldo Rabello de Aguiar Vallim Filho1Leilton Santos da Silva2Leandro Augusto da Silva3Postgraduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, Rua da Consolação, 896, Prédio 30—Consolação, São Paulo 01302-907, BrazilComputer Science Dept., Mackenzie Presbyterian University, Rua da Consolação, 896, Prédio 31—Consolação, São Paulo 01302-907, BrazilEMAE—Metropolitan Company of Water & Energy, Avenida Nossa Senhora do Sabará, 5312—Vila Emir, São Paulo 04447-902, BrazilPostgraduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, Rua da Consolação, 896, Prédio 30—Consolação, São Paulo 01302-907, BrazilIndustry 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, <i>k</i>-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.https://www.mdpi.com/1424-8220/19/19/4342predictive maintenancecondition-based maintenanceartificial neural networkvibratory analysissmart industryindustry maintenance |
spellingShingle | Gustavo Scalabrini Sampaio Arnaldo Rabello de Aguiar Vallim Filho Leilton Santos da Silva Leandro Augusto da Silva Prediction of Motor Failure Time Using An Artificial Neural Network Sensors predictive maintenance condition-based maintenance artificial neural network vibratory analysis smart industry industry maintenance |
title | Prediction of Motor Failure Time Using An Artificial Neural Network |
title_full | Prediction of Motor Failure Time Using An Artificial Neural Network |
title_fullStr | Prediction of Motor Failure Time Using An Artificial Neural Network |
title_full_unstemmed | Prediction of Motor Failure Time Using An Artificial Neural Network |
title_short | Prediction of Motor Failure Time Using An Artificial Neural Network |
title_sort | prediction of motor failure time using an artificial neural network |
topic | predictive maintenance condition-based maintenance artificial neural network vibratory analysis smart industry industry maintenance |
url | https://www.mdpi.com/1424-8220/19/19/4342 |
work_keys_str_mv | AT gustavoscalabrinisampaio predictionofmotorfailuretimeusinganartificialneuralnetwork AT arnaldorabellodeaguiarvallimfilho predictionofmotorfailuretimeusinganartificialneuralnetwork AT leiltonsantosdasilva predictionofmotorfailuretimeusinganartificialneuralnetwork AT leandroaugustodasilva predictionofmotorfailuretimeusinganartificialneuralnetwork |