Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/95701 https://doi.org/10.3390/app11167685 |
Resumo: | The availability maximization is a goal for any organization because the equipment downtime implies high non-production costs and, additionally, the abnormal stopping and restarting usually imply loss of product’s quality. In this way, a method for predicting the equipment’s health state is vital to maintain the production flow as well as to plan maintenance intervention strategies. This paper presents a maintenance prediction approach based on sensing data managed by Hidden Markov Models (HMM). To do so, a diagnosis of drying presses in a pulp industry is used as case study, which is done based on data collected every minute for three years and ten months. This paper presents an approach to manage a multivariate analysis, in this case merging the values of sensors, and optimizing the observable states to insert into a HMM model, which permits to identify three hidden states that characterize the equipment’s health state: “Proper Function”, “Alert state”, and “Equipment Failure”. The research described in this paper demonstrates how an equipment health diagnosis can be made using the HMM, through the collection of observations from various sensors, without information of machine failures occurrences. The approach developed demonstrated to be robust, even the complexity of the system, having the potential to be generalized to any other type of equipment. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
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Maintenance Prediction through Sensing Using Hidden Markov Models—A Case StudyBig dataCluster analysisCondition-based maintenanceHidden Markov ModelsIndustrial sensorsPrincipal component analysisThe availability maximization is a goal for any organization because the equipment downtime implies high non-production costs and, additionally, the abnormal stopping and restarting usually imply loss of product’s quality. In this way, a method for predicting the equipment’s health state is vital to maintain the production flow as well as to plan maintenance intervention strategies. This paper presents a maintenance prediction approach based on sensing data managed by Hidden Markov Models (HMM). To do so, a diagnosis of drying presses in a pulp industry is used as case study, which is done based on data collected every minute for three years and ten months. This paper presents an approach to manage a multivariate analysis, in this case merging the values of sensors, and optimizing the observable states to insert into a HMM model, which permits to identify three hidden states that characterize the equipment’s health state: “Proper Function”, “Alert state”, and “Equipment Failure”. The research described in this paper demonstrates how an equipment health diagnosis can be made using the HMM, through the collection of observations from various sensors, without information of machine failures occurrences. The approach developed demonstrated to be robust, even the complexity of the system, having the potential to be generalized to any other type of equipment. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.MDPI2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/95701http://hdl.handle.net/10316/95701https://doi.org/10.3390/app11167685eng2076-3417Martins, AlexandreFonseca, Inácio de Sousa Adelino daFarinha, José TorresReis, JoãoCardoso, António J. Marquesinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-05-25T03:29:31Zoai:estudogeral.uc.pt:10316/95701Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:14:07.478779Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study |
title |
Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study |
spellingShingle |
Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study Martins, Alexandre Big data Cluster analysis Condition-based maintenance Hidden Markov Models Industrial sensors Principal component analysis |
title_short |
Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study |
title_full |
Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study |
title_fullStr |
Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study |
title_full_unstemmed |
Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study |
title_sort |
Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study |
author |
Martins, Alexandre |
author_facet |
Martins, Alexandre Fonseca, Inácio de Sousa Adelino da Farinha, José Torres Reis, João Cardoso, António J. Marques |
author_role |
author |
author2 |
Fonseca, Inácio de Sousa Adelino da Farinha, José Torres Reis, João Cardoso, António J. Marques |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Martins, Alexandre Fonseca, Inácio de Sousa Adelino da Farinha, José Torres Reis, João Cardoso, António J. Marques |
dc.subject.por.fl_str_mv |
Big data Cluster analysis Condition-based maintenance Hidden Markov Models Industrial sensors Principal component analysis |
topic |
Big data Cluster analysis Condition-based maintenance Hidden Markov Models Industrial sensors Principal component analysis |
description |
The availability maximization is a goal for any organization because the equipment downtime implies high non-production costs and, additionally, the abnormal stopping and restarting usually imply loss of product’s quality. In this way, a method for predicting the equipment’s health state is vital to maintain the production flow as well as to plan maintenance intervention strategies. This paper presents a maintenance prediction approach based on sensing data managed by Hidden Markov Models (HMM). To do so, a diagnosis of drying presses in a pulp industry is used as case study, which is done based on data collected every minute for three years and ten months. This paper presents an approach to manage a multivariate analysis, in this case merging the values of sensors, and optimizing the observable states to insert into a HMM model, which permits to identify three hidden states that characterize the equipment’s health state: “Proper Function”, “Alert state”, and “Equipment Failure”. The research described in this paper demonstrates how an equipment health diagnosis can be made using the HMM, through the collection of observations from various sensors, without information of machine failures occurrences. The approach developed demonstrated to be robust, even the complexity of the system, having the potential to be generalized to any other type of equipment. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/95701 http://hdl.handle.net/10316/95701 https://doi.org/10.3390/app11167685 |
url |
http://hdl.handle.net/10316/95701 https://doi.org/10.3390/app11167685 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2076-3417 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134038597828608 |