Guolei Huang, Yifan Liu, Jianjian Xin, Tiantian Bao
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Assessment of Hull and Propeller Performance Degradation Based on TSO-GA-LSTM
Evaluating the degradation of hull and ship performance and exploring their degradation pathways is crucial for developing scientific and reasonable ship maintenance plans. This paper proposes a two-stage optimization (TSO) algorithm that combines the Genetic Algorithm (GA) and Long Short-Term Memory (LSTM) network, capable of simultaneously optimizing input features and model parameters to enhance the accuracy and generalization ability of speed prediction models. Additionally, a performance degradation assessment method based on speed loss is provided, aimed at evaluating the degradation of hull and propeller performance, as well as extracting the performance degradation paths. The results indicated that the proposed TSO-LSTM-GA algorithm significantly outperformed existing baseline models. Furthermore, the provided performance degradation assessment method demonstrated certain effectiveness on the target ship data, with a measured degradation rate of 0.00344 kn/d and a performance degradation of 9.569% over 478 days, corresponding to an annual speed loss of 1.257 kn.
期刊介绍:
Journal of Marine Science and Engineering (JMSE; ISSN 2077-1312) is an international, peer-reviewed open access journal which provides an advanced forum for studies related to marine science and engineering. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.