- Tytuł:
- Recurrent neural network optimization for wind turbine condition prognosis
- Autorzy:
-
Kerboua, Adlen
Kelaiaia, Ridha - Powiązania:
- https://bibliotekanauki.pl/articles/2096205.pdf
- Data publikacji:
- 2022
- Wydawca:
- Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
- Tematy:
-
forecasting
recurrent network
optimization
hyperparameters
loss
prognozowanie
optymalizacja
sieci rekurencyjne
strata - Opis:
- This research focuses on employing Recurrent Neural Networks (RNN) to prognosis a wind turbine operation’s health from collected vibration time series data, by using several memory cell variations, including Long Short Time Memory (LSTM), Bilateral LSTM (BiLSTM), and Gated Recurrent Unit (GRU), which are integrated into various architectures. We tune the training hyperparameters as well as the adapted depth and recurrent cell number of the proposed networks to obtain the most accurate predictions. Tuning those parameters is a hard task and depends widely on the experience of the designer. This can be resolved by integrating the training process in a Bayesian optimization loop where the loss is considered as the objective function to minimize. The obtained results show the effectiveness of the proposed method, which generates more accurate recurrent models with a more accurate prognosis of the operating state of the wind turbine than those generated using trivial training parameters.
- Źródło:
-
Diagnostyka; 2022, 23, 3; art. no. 2022301
1641-6414
2449-5220 - Pojawia się w:
- Diagnostyka
- Dostawca treści:
- Biblioteka Nauki
Artykuł