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Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press
- Source :
- Energies; Volume 14; Issue 21; Pages: 6958, Energies, Vol 14, Iss 6958, p 6958 (2021)
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options.
- Subjects :
- Technology
Multivariate statistics
Control and Optimization
Computer science
GRU
Energy Engineering and Power Technology
Machine learning
computer.software_genre
Predictive maintenance
predictive maintenance
LSTM
recurrent neural network
paper press
Autoregressive integrated moving average
Electrical and Electronic Engineering
Engineering (miscellaneous)
Hyperparameter
Artificial neural network
Renewable Energy, Sustainability and the Environment
business.industry
Univariate
Statistical model
Recurrent neural network
Artificial intelligence
business
computer
Energy (miscellaneous)
Subjects
Details
- Language :
- English
- ISSN :
- 19961073
- Database :
- OpenAIRE
- Journal :
- Energies; Volume 14; Issue 21; Pages: 6958
- Accession number :
- edsair.doi.dedup.....3fd9ccf9fd31b5c6aa7d4861ad66be02
- Full Text :
- https://doi.org/10.3390/en14216958