Prethodno priopćenje

https://doi.org/10.18048/2020.58.02.

Use of Artificial Neural Network for Estimation of Propeller Torque Values in a CODLAG Propulsion System

Sandi Baressi Šegota orcid.org/0000-0002-3015-1024 ; University of Rijeka, Faculty of Engineering
Daniel Štifanić orcid.org/0000-0001-9396-2441 ; University of Rijeka, Faculty of Engineering
Kazuhiro Ohkura ; Hiroshiuma University, 1-4-1, Kagamiyama, Higashi-Hiroshima
Zlatan Car orcid.org/0000-0003-2817-9252 ; University of Rijeka, Faculty of Engineering

Sažetak

An artificial neural network (ANN) approach is proposed to the problem of estimating the propeller torques of a frigate using combined diesel, electric and gas (CODLAG) propulsion system. The authors use a multilayer perceptron (MLP) feed-forward ANN trained with data from a dataset which describes the decay state coefficients as outputs and system parameters as inputs – with a goal of determining the propeller torques, removing the decay state coefficients and using the torque values of the starboard and port propellers as outputs. A total of 53760 ANNs are trained – 26880 for each of the propellers, with a total 8960 parameter combinations. The results are evaluated using mean absolute error (MAE) and coefficient of determination (R2). Best results for the starboard propeller are MAE of 2.68 [Nm], and MAE of 2.58 [Nm] for the port propeller with following ANN configurations respectively: 2 hidden layers with 32 neurons and identity activation and 3 hidden layers with 16, 32 and 16 neurons and identity activation function. Both configurations achieve R2 value higher than 0.99.

Ključne riječi

artificial neural network; machine learning; CODLAG; propeller torque estimation; 25 propulsion systems

Hrčak ID:

240865

URI

https://hrcak.srce.hr/240865

Datum izdavanja:

30.6.2020.

Posjeta: 946 *