Please use this identifier to cite or link to this item:
Title: Anticipating Future Behavior of an Industrial Press Using LSTM Networks
Authors: Mateus, Balduíno César
Mendes, Mateus 
Farinha, José Torres 
Cardoso, António Marques
Keywords: time series prediction; LSTM prediction; deep learning prediction; predictive maintenance
Issue Date: 2021
Project: POCI-01-0145-FEDER-029494 
Marie Sklodowvska-Curie grant agreement 871284 project SSHARE 
Project 01/SAICT/2016 nº 022153 
Serial title, monograph or event: Applied Sciences (Switzerland)
Volume: 11
Issue: 13
Abstract: Predictive maintenance is very important in industrial plants to support decisions aiming to maximize maintenance investments and equipment’s availability. This paper presents predictive models based on long short-term memory neural networks, applied to a dataset of sensor readings. The aim is to forecast future equipment statuses based on data from an industrial paper press. The datasets contain data from a three-year period. Data are pre-processed and the neural networks are optimized to minimize prediction errors. The results show that it is possible to predict future behavior up to one month in advance with reasonable confidence. Based on these results, it is possible to anticipate and optimize maintenance decisions, as well as continue research to improve the reliability of the model.
ISSN: 2076-3417
DOI: 10.3390/app11136101
Rights: openAccess
Appears in Collections:I&D ISR - Artigos em Revistas Internacionais

Show full item record

Google ScholarTM




This item is licensed under a Creative Commons License Creative Commons