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Recurrent neural network optimization for wind turbine condition prognosis

100%

Kerboua A. , Kelaiaia R.

Diagnostyka

2022

tom 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.

Intelligent LQI-based wireless sensor network applied to ZigBee positioning system

100%

Mon Y. J. , Lin C. M.

2013

tom R. 89, nr 5

241-244

In this paper, a link quality indicator (LQI) based wireless sensor network (WSN) constructed by a recurrent fuzzy neural network (RFNN) is developed as a ZigBee Positioning System (ZPS) to monitor and realize the tag of 802.15.4/ZigBee locations. First, the performance of LQI is demonstrated, then it is applied to develop a ZPS which is used to verify the performance of indoor location identification. Finally, an RFNN is used to combine with the ZPS to develop a location system, and it can be applied for children’s position monitoring. The experimental results demonstrate good positioning performance has been achieved by the proposed location system.

W artykule opisano sieć czujników bezprzewodowych zbudowaną za pomocą sieci neuronowej RFNN, na bazie metody LQI. Ma ona zastosowanie w protokole 802.15.4/ZigBee jako blok lokalizacji (ZigBee Positioning System). Omówione zostało zastosowanie LQI, który został wykorzystany w projektowaniu ZPS. Na koniec wykorzystano RFNN oraz ZPS w budowie systemu lokalizacji. Badania eksperymentalne potwierdziły skuteczność działania proponowanego systemu.