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Recurrent neural network optimization for wind turbine condition prognosis

Kerboua Adlen, Kelaiaia Ridha

Diagnostyka

2022

Vol. 23, No. 3

art. no. 2022301

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.

Evolving adaptive recurrent networks with Developmental Symbolic Encoding

Suchorzewski M.

Metody Informatyki Stosowanej

2010

nr 1 (22)

175-187

In this paper we are concerned with evolutionary synthesis of recurrent networks capable of learning in the environment. First, we define the model of network we aim to evolve, which is weightless recurrent network of basic arithmetic nodes. Next, we propose a developmental genetic representation for the network, along with some genetic operators for it. The representation bears some important characteristics such as closure and completeness. Most notably, however, it features modularity and scalability, which we demonstrate on a parity problem. Finally, we evolve the network capable of successful learning in some narrow problem domain. The result shows, that for a given problem domain, evolutionary approach may produce networks performing better than generic neural networks.