Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/95701
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Martins, Alexandre | - |
dc.contributor.author | Fonseca, Inácio de Sousa Adelino da | - |
dc.contributor.author | Farinha, José Torres | - |
dc.contributor.author | Reis, João | - |
dc.contributor.author | Cardoso, António J. Marques | - |
dc.date.accessioned | 2021-09-08T15:41:03Z | - |
dc.date.available | 2021-09-08T15:41:03Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 2076-3417 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/95701 | - |
dc.description.abstract | 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. | pt |
dc.language.iso | eng | pt |
dc.publisher | MDPI | pt |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/871284/EU/Self-sufficient humidity to electricity Innovative Radiant Adsorption System Toward Net Zero Energy Buildings | pt |
dc.relation | POCI-01-0145-FEDER-029494 | pt |
dc.relation | PTDC/EEI-EEE/29494/2017 | pt |
dc.relation | UIDB/04131/2020 | pt |
dc.relation | UIDP/04131/2020 | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | Big data | pt |
dc.subject | Cluster analysis | pt |
dc.subject | Condition-based maintenance | pt |
dc.subject | Hidden Markov Models | pt |
dc.subject | Industrial sensors | pt |
dc.subject | Principal component analysis | pt |
dc.title | Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study | pt |
dc.type | article | - |
degois.publication.firstPage | 7685 | pt |
degois.publication.issue | 16 | pt |
degois.publication.title | Applied Sciences | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.3390/app11167685 | pt |
degois.publication.volume | 11 | pt |
dc.date.embargo | 2021-01-01 | * |
uc.date.periodoEmbargo | 0 | pt |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.fulltext | Com Texto completo | - |
item.languageiso639-1 | en | - |
crisitem.author.researchunit | CEMMPRE - Centre for Mechanical Engineering, Materials and Processes | - |
crisitem.author.orcid | 0000-0002-9694-8079 | - |
Appears in Collections: | I&D CEMMPRE - Artigos em Revistas Internacionais |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
applsci-11-07685.pdf | 6.05 MB | Adobe PDF | View/Open |
SCOPUSTM
Citations
1
checked on Nov 9, 2022
WEB OF SCIENCETM
Citations
1
checked on Nov 15, 2022
Page view(s)
142
checked on Apr 24, 2024
Download(s)
134
checked on Apr 24, 2024
Google ScholarTM
Check
Altmetric
Altmetric
This item is licensed under a Creative Commons License