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https://hdl.handle.net/10316/100622
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 PTDC/EEI-EEE/29494/2017 UIDB/04131/2020 UIDP/04131/2020 Project 01/SAICT/2016 nº 022153 UIDB/00285/2020 |
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. | URI: | https://hdl.handle.net/10316/100622 | ISSN: | 2076-3417 | DOI: | 10.3390/app11136101 | Rights: | openAccess |
Appears in Collections: | I&D ISR - Artigos em Revistas Internacionais |
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