基于小波变换与改进时间序列模型的船舶升沉运动预测方法

doi:10.3976/j.issn.1002-4026.20240019

Abstract

Abstract:

Lag in detecting ship heave motion signals severely affects the performance of ocean heave compensation systems. Therefore, accurate heave motion prediction can effectively improve the stability and real-time performance of these systems. To improve the engineering practicability of a heave motion prediction model, we designed an autoregressive time-series model featuring high calculation efficiency, simple programing, and a small accumulation error. Moreover, to further address the poor adaptability of the model to nonlinear and nonstationary complex sea conditions and long-term predictions, we developed a combined prediction model based on wavelet transform and improved autoregression using the wavelet multiscale analysis method and achieved online multistep prediction of heave motions by decomposing and transforming historical data, reconstructing sub-sequence prediction, and forecasting data synthesis. Finally, theoretical testing and experiments were conducted on stationary random waveforms and nonstationary waveforms measured on ships. The analysis results show that the combined model exhibits good prediction performance and can effectively reduce the control error of the ocean heave compensation system caused by the lag in the heave motion signal detection.

Key words: ocean engineering, heave compensation, time-series prediction, wavelet transformation

CLC Number:

TE58

LIU Zhizhen, HUANG Lumeng, SUN Yapeng, ZHANG Ying, LIU Zhendong. Ship heave motion prediction method based on wavelet transform and improved time series model[J].Shandong Science, 2024, 37(6): 1-11.

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References 21

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预测步数	随机波			实测波
预测步数	δ_MAE/m	δ_RMSE/m	J_D	δ_MAE/m	δ_RMSE/m	J_D
1	0.000 1	0.000 2	0.999 8	0.035 0	0.056 3	0.902 0
3	0.007 1	0.008 3	0.990 4	0.150 2	0.210 5	0.572 5
5	0.047 7	0.055 9	0.935 0	0.230 1	0.303 2	0.332 5

预测步数	δ_MAE/m	δ_RMSE/m	J_D
1	0.000 2	0.000 3	0.999 3
3	0.005 6	0.007 9	0.94 2
5	0.015 5	0.020 2	0.955 0

Ship heave motion prediction method based on wavelet transform and improved time series model

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