Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/95701
Title: Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study
Authors: Martins, Alexandre
Fonseca, Inácio de Sousa Adelino da 
Farinha, José Torres 
Reis, João
Cardoso, António J. Marques
Keywords: Big data; Cluster analysis; Condition-based maintenance; Hidden Markov Models; Industrial sensors; Principal component analysis
Issue Date: 2021
Publisher: MDPI
Project: info:eu-repo/grantAgreement/EC/H2020/871284/EU/Self-sufficient humidity to electricity Innovative Radiant Adsorption System Toward Net Zero Energy Buildings 
POCI-01-0145-FEDER-029494 
PTDC/EEI-EEE/29494/2017 
UIDB/04131/2020 
UIDP/04131/2020 
Serial title, monograph or event: Applied Sciences
Volume: 11
Issue: 16
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.
URI: http://hdl.handle.net/10316/95701
ISSN: 2076-3417
DOI: 10.3390/app11167685
Rights: openAccess
Appears in Collections:I&D CEMUC - Artigos em Revistas Internacionais

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