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Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press
| Content Provider | MDPI |
|---|---|
| Author | Mateus, Balduíno César Mendes, Mateus Farinha, José Torres Assis, Rui Cardoso, António Marques |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 6958 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en14216958 |
| Journal | Energies |
| Issue Number | 21 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-10-22 |
| Access Restriction | Open |
| Subject Keyword | Energies Information and Library Science Lstm Recurrent Neural Network Gru Paper Press Predictive Maintenance |
| Content Type | Text |
| Resource Type | Article |
