Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/100596
Title: Short and long forecast to implement predictive maintenance in a pulp industry
Authors: Rodrigues, João Antunes
Farinha, José Manuel Torres 
Mendes, Mateus 
Mateus, Ricardo
Cardoso, António
Keywords: predictive maintenance; condition based maintenance; time series; artificial neural networks; forecasting
Issue Date: 2021
Project: UIDB/00285/2020 
FCT and FEDER Project 01/SAICT/2016 nº 022153 
POCI-01-0145-FEDER-029494 
PTDC/EEI-EEE/29494/2017 
UIDB/04131/2020 
UIDP/04131/2020 
Marie Sklodowvska-Curie grant agreement 871284 project SSHARE 
Serial title, monograph or event: Eksploatacja i Niezawodnosc
Volume: 24
Issue: 1
Abstract: Predictive maintenance is very important for effective prevention of failures in an industry. The present paper describes a case study where a wood chip pump system was analyzed, and a predictive model was proposed. An Ishikawa diagram and FMECA are used to identify possible causes for system failure. The Chip Wood has several sensors installed to monitor the working conditions and system state. The authors propose a variation of exponential smoothing technique for short time forecasting and an artificial neural network for long time forecasting. The algorithms were integrated into a dashboard for online condition monitoring, where the users are alerted when a variable is determined or predicted to get out of the expected range. Experimental results show prediction errors in general less than 10 %. The proposed technique may be of help in monitoring and maintenance of the asset, aiming at greater availability.
URI: http://hdl.handle.net/10316/100596
ISSN: 15072711
DOI: 10.17531/ein.2022.1.5
Rights: openAccess
Appears in Collections:I&D ISR - Artigos em Revistas Internacionais
I&D CEMMPRE - Artigos em Revistas Internacionais

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