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Brodogradnja : An International Journal of Naval Architecture and Ocean Engineering for Research and Development, Vol. 76 No. 1, 2025.

Izvorni znanstveni članak

https://doi.org/10.21278/brod76101

Short-term prediction of trimaran load based on data driven technology

Haoyun Tang ; Navigation College, Dalian Maritime University, Dalian 116026, China
Rui Zhu ; Navigation College, Dalian Maritime University, Dalian 116026, China
Qian Wan ; College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China *
Deyuan Ren ; Navigation College, Dalian Maritime University, Dalian 116026, China

* Dopisni autor.

Puni tekst: engleski pdf 2.741 Kb

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Sažetak

Due to the complex flow interference by side hulls, load prediction has been one of the obstacles to achieve structural health monitoring and intelligent navigation of trimarans. According to the machine learning theory, a short-term prediction method towards trimaran loads is studied by using an optimized data-driven model. In the research, the monitoring data from a trimaran model test is applied for the training and testing of long short-term memory (LSTM) neural network. The impact analysis on the factors such as input length, neuron number, artificial neural network (ANN) optimizer, and output scope, are taken. To highlight the trimaran high-frequency load fluctuation and improve the prediction accuracy, the LSTM neural network combines with different signal decomposition algorithms, such as Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and Variational Mode Decomposition (VMD). Through the comprehensive comparison of statistical indicators at different positions and wave environment, the VMD-LSTM model is selected by considering its highest load prediction ability among these artificial neural network models. The research will improve the load prediction accuracy in the structural health monitoring systems and offer effective technical support for intelligent unmanned trimarans.

Ključne riječi

Trimaran; wave load; LSTM neural network; signal decomposition

Hrčak ID:

326495

URI

https://hrcak.srce.hr/326495

Datum izdavanja:

1.1.2025.

Posjeta: 0 *