Prediction of motor failure time using an artificial neural network

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dc.contributor.authorSampaio G.S.
dc.contributor.authorFilho A.R.A.V.
dc.contributor.authorda Silva L.S.
dc.contributor.authorda Silva L.A.
dc.date.accessioned2024-03-12T23:51:45Z
dc.date.available2024-03-12T23:51:45Z
dc.date.issued2019
dc.description.abstract© 2019 by the authors. Licensee MDPI, Basel, Switzerland.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.
dc.description.issuenumber19
dc.description.volume19
dc.identifier.doi10.3390/s19194342
dc.identifier.issn1424-8220
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/35192
dc.relation.ispartofSensors (Switzerland)
dc.rightsAcesso Aberto
dc.subject.otherlanguageArtificial neural network
dc.subject.otherlanguageCondition-based maintenance
dc.subject.otherlanguageIndustry maintenance
dc.subject.otherlanguagePredictive maintenance
dc.subject.otherlanguageSmart industry
dc.subject.otherlanguageVibratory analysis
dc.titlePrediction of motor failure time using an artificial neural network
dc.typeArtigo
local.scopus.citations37
local.scopus.eid2-s2.0-85073445489
local.scopus.subjectCondition based maintenance
local.scopus.subjectCorrective maintenance
local.scopus.subjectK fold cross validations
local.scopus.subjectMachine learning techniques
local.scopus.subjectNeural network training
local.scopus.subjectPredictive maintenance
local.scopus.subjectScheduled maintenance
local.scopus.subjectVibratory analysis
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85073445489&origin=inward
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