FORECAST OF MULTIVARIATE TIME SERIES SAMPLED FROM INDUSTRIAL MACHINERY SENSORS
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Operations & Production Management (Online) |
Texto Completo: | https://bjopm.org.br/bjopm/article/view/607 |
Resumo: | Goal: To evaluate the performance of a set of forecasting methods in the prediction of future values on a dataset of time series collected from sensors installed in an industrial gas turbine. Design/Methodology/Approach: Forecasting methods tested include the use of multivariate and univariate neural networks (FNN and LSTM), exponential smoothing and ARIMA models. Results: Results show that the use of ARIMA models to forecast on the dataset is the best default method to apply, and is the only forecasting method that consistently beats a simple naïve no-change model. Limitation of the investigation: There was a focus on evaluating neural networks. This limited resources available to evaluate other forecasting methods. There is no guarantee that it would not be possible to find neural networks capable of yielding better forecasts than the ones achieved by the best performing methods in this research. Practical implications: The broadest possible implications of the results are that the best default method to forecast industrial machinery time series is the use of ARIMA models. Additionally, neural networks are not capable of beating methods well stablished within the forecasting community, namely ARIMA models. Originality/Value: To the best of the authors’ knowledge, there is a scarce amount of published evaluations of multiple forecasting methods on data from real machines. This knowledge is useful for the understanding of the best forecasting methods available for the estimation of machine’s RUL using sensor time series. |
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Brazilian Journal of Operations & Production Management (Online) |
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FORECAST OF MULTIVARIATE TIME SERIES SAMPLED FROM INDUSTRIAL MACHINERY SENSORSprognosticstime seriesforecastingneural networksARIMAGoal: To evaluate the performance of a set of forecasting methods in the prediction of future values on a dataset of time series collected from sensors installed in an industrial gas turbine. Design/Methodology/Approach: Forecasting methods tested include the use of multivariate and univariate neural networks (FNN and LSTM), exponential smoothing and ARIMA models. Results: Results show that the use of ARIMA models to forecast on the dataset is the best default method to apply, and is the only forecasting method that consistently beats a simple naïve no-change model. Limitation of the investigation: There was a focus on evaluating neural networks. This limited resources available to evaluate other forecasting methods. There is no guarantee that it would not be possible to find neural networks capable of yielding better forecasts than the ones achieved by the best performing methods in this research. Practical implications: The broadest possible implications of the results are that the best default method to forecast industrial machinery time series is the use of ARIMA models. Additionally, neural networks are not capable of beating methods well stablished within the forecasting community, namely ARIMA models. Originality/Value: To the best of the authors’ knowledge, there is a scarce amount of published evaluations of multiple forecasting methods on data from real machines. This knowledge is useful for the understanding of the best forecasting methods available for the estimation of machine’s RUL using sensor time series.Brazilian Association for Industrial Engineering and Operations Management (ABEPRO)2020-02-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed Articleapplication/pdfhttps://bjopm.org.br/bjopm/article/view/60710.14488/BJOPM.2020.010Brazilian Journal of Operations & Production Management; Vol. 17 No. 1 (2020): March, 2020; 1-122237-8960reponame:Brazilian Journal of Operations & Production Management (Online)instname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPROenghttps://bjopm.org.br/bjopm/article/view/607/913Copyright (c) 2020 Heron Felipe Rosas dos Santos, Leila Weitzel Coelho da Silva, Ana Paula Barbosa Sobralinfo:eu-repo/semantics/openAccessdos Santos, Heron Felipe Rosasda Silva, Leila Weitzel CoelhoSobral, Ana Paula Barbosa2020-02-07T10:25:50Zoai:ojs.bjopm.org.br:article/607Revistahttps://bjopm.org.br/bjopmONGhttps://bjopm.org.br/bjopm/oaibjopm.journal@gmail.com2237-89601679-8171opendoar:2023-03-13T09:45:19.465280Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
FORECAST OF MULTIVARIATE TIME SERIES SAMPLED FROM INDUSTRIAL MACHINERY SENSORS |
title |
FORECAST OF MULTIVARIATE TIME SERIES SAMPLED FROM INDUSTRIAL MACHINERY SENSORS |
spellingShingle |
FORECAST OF MULTIVARIATE TIME SERIES SAMPLED FROM INDUSTRIAL MACHINERY SENSORS dos Santos, Heron Felipe Rosas prognostics time series forecasting neural networks ARIMA |
title_short |
FORECAST OF MULTIVARIATE TIME SERIES SAMPLED FROM INDUSTRIAL MACHINERY SENSORS |
title_full |
FORECAST OF MULTIVARIATE TIME SERIES SAMPLED FROM INDUSTRIAL MACHINERY SENSORS |
title_fullStr |
FORECAST OF MULTIVARIATE TIME SERIES SAMPLED FROM INDUSTRIAL MACHINERY SENSORS |
title_full_unstemmed |
FORECAST OF MULTIVARIATE TIME SERIES SAMPLED FROM INDUSTRIAL MACHINERY SENSORS |
title_sort |
FORECAST OF MULTIVARIATE TIME SERIES SAMPLED FROM INDUSTRIAL MACHINERY SENSORS |
author |
dos Santos, Heron Felipe Rosas |
author_facet |
dos Santos, Heron Felipe Rosas da Silva, Leila Weitzel Coelho Sobral, Ana Paula Barbosa |
author_role |
author |
author2 |
da Silva, Leila Weitzel Coelho Sobral, Ana Paula Barbosa |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
dos Santos, Heron Felipe Rosas da Silva, Leila Weitzel Coelho Sobral, Ana Paula Barbosa |
dc.subject.por.fl_str_mv |
prognostics time series forecasting neural networks ARIMA |
topic |
prognostics time series forecasting neural networks ARIMA |
description |
Goal: To evaluate the performance of a set of forecasting methods in the prediction of future values on a dataset of time series collected from sensors installed in an industrial gas turbine. Design/Methodology/Approach: Forecasting methods tested include the use of multivariate and univariate neural networks (FNN and LSTM), exponential smoothing and ARIMA models. Results: Results show that the use of ARIMA models to forecast on the dataset is the best default method to apply, and is the only forecasting method that consistently beats a simple naïve no-change model. Limitation of the investigation: There was a focus on evaluating neural networks. This limited resources available to evaluate other forecasting methods. There is no guarantee that it would not be possible to find neural networks capable of yielding better forecasts than the ones achieved by the best performing methods in this research. Practical implications: The broadest possible implications of the results are that the best default method to forecast industrial machinery time series is the use of ARIMA models. Additionally, neural networks are not capable of beating methods well stablished within the forecasting community, namely ARIMA models. Originality/Value: To the best of the authors’ knowledge, there is a scarce amount of published evaluations of multiple forecasting methods on data from real machines. This knowledge is useful for the understanding of the best forecasting methods available for the estimation of machine’s RUL using sensor time series. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-02-06 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/607 10.14488/BJOPM.2020.010 |
url |
https://bjopm.org.br/bjopm/article/view/607 |
identifier_str_mv |
10.14488/BJOPM.2020.010 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/607/913 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
dc.source.none.fl_str_mv |
Brazilian Journal of Operations & Production Management; Vol. 17 No. 1 (2020): March, 2020; 1-12 2237-8960 reponame:Brazilian Journal of Operations & Production Management (Online) instname:Associação Brasileira de Engenharia de Produção (ABEPRO) instacron:ABEPRO |
instname_str |
Associação Brasileira de Engenharia de Produção (ABEPRO) |
instacron_str |
ABEPRO |
institution |
ABEPRO |
reponame_str |
Brazilian Journal of Operations & Production Management (Online) |
collection |
Brazilian Journal of Operations & Production Management (Online) |
repository.name.fl_str_mv |
Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO) |
repository.mail.fl_str_mv |
bjopm.journal@gmail.com |
_version_ |
1797051461028282368 |