集成学习算法在船舶辐射噪声预报中的应用研究

强以铭; 陈奕宏; 裴雨晴; 庞业珍; 谢硕

doi:10.11993/j.issn.2096-3920.2025-0111

集成学习算法在船舶辐射噪声预报中的应用研究

doi: 10.11993/j.issn.2096-3920.2025-0111

强以铭^{1, 3,},
陈奕宏^{1, 2, ,},
裴雨晴^{1, 2},
庞业珍^{1, 2},
谢硕¹

1.
中国船舶科学研究中心, 江苏无锡, 214082
2.
船舶振动噪声重点研究室, 江苏无锡, 214082
3.
上海交通大学, 船舶海洋与建筑工程学院, 上海, 200240

详细信息

作者简介:
强以铭(1994-) , 女, 硕士, 工程师, 主要研究方向为数据科学

通讯作者:
陈奕宏(1980-), 男, 硕士, 研究员, 主要研究方向为水声学.

中图分类号: TJ630; U661
计量
- 文章访问数: 81
- HTML全文浏览量: 35
- PDF下载量: 11
- 被引次数: 0
出版历程
- 收稿日期: 2025-08-22
- 修回日期: 2025-09-12
- 录用日期: 2025-09-15
- 网络出版日期: 2025-11-26

Application of Ensemble Learning Algorithms on Ship Radiation Noise Prediction

QIANG Yiming^{1, 3
,},
CHEN Yihong^{1, 2
, ,},
PEI Yuqing^{1, 2},
PANG Yezhen^{1, 2},
XIE Shuo¹

1.
China Ship Scientific Research Center, Wuxi 214082, China
2.
Ship Vibration and Noise Key Laboratory, Wuxi 214082, China
3.
School of Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China

摘要

摘要: 船舶多振动源产生的辐射噪声会严重影响其舒适性与隐身性。准确预测其辐射噪声水平与分布, 可为船舶设计阶段的减振降噪优化提供关键支撑。针对船舶振动源数量多以及噪声辐射机制复杂的问题, 文中首先采用集成学习算法中的随机森林与梯度提升树算法, 对不同工况、不同测点的1/3倍频程噪声声压级开展快速预报, 并与贝叶斯岭回归模型的预报效果进行对比验证。4种测试工况的验证结果显示, 集成学习算法在3种工况下的预报效果优于贝叶斯岭回归, 平均绝对误差均小于5 dB; 进一步对上述模型进行优化, 通过在不同层次构建集成学习算法与线性算法的基础单元并组合, 形成辐射噪声联合预报方案, 其精度较单一集成学习算法提升1.5 dB。文中所提集成学习算法及联合预报方案可为船舶辐射噪声的快速精准分析提供有效技术工具。
- 船舶辐射噪声 /
- 噪声预报 /
- 集成学习算法 /
- 减振降噪
Abstract: The radiation noise generated by multiple vibration sources on ships can seriously affect their comfort and stealth performance. Accurately predicting the radiation noise level and distribution can provide crucial support for the vibration and noise reduction optimization during the ship design stage. In response to the problem of numerous vibration sources in ships and the complex noise radiation mechanism, this paper first employed the random forest and gradient boosting tree algorithms from the ensemble learning algorithms to conduct rapid prediction of the 1/3 octave band noise sound pressure level under different operating conditions and at different measurement points. The prediction results were then compared and verified with those of the Bayesian ridge regression model. The verification results of the four test conditions show that the ensemble learning algorithm outperforms the Bayesian ridge regression in all three conditions, with an average absolute error of less than 5 dB. Further optimization of the above model is conducted by constructing the basic units of the ensemble learning algorithm and the linear algorithm at different levels and combining them to form a joint radiation noise prediction scheme. Its accuracy is improved by 1.5 dB compared to the single ensemble learning algorithm. The ensemble learning algorithm and the joint prediction scheme proposed in this paper can provide effective technical tools for the rapid and accurate analysis of ship radiation noise.
- ship radiation noise /
- noise prediction /
- ensemble learning algorithm /
- vibration and noise reduction

HTML全文

图 1 样本采集试验装置示意图

Figure 1. Schematic diagram of sample collection test device

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图 2 样本训练与验证分割方式

Figure 2. Segmentation method for sample training and verification

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图 3 RF与GBDT模型结构示意图

Figure 3. Structure diagram of RF and GBDT models

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图 4 不同频率下预报模型噪声等级预报结果

Figure 4. Prediction results of noise level of prediction model at different frequencies

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图 5 不同测点下模型噪声等级预报结果

Figure 5. Prediction results of model noise level at different measuring points

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图 6 优化后模型结构示意图

Figure 6. Schematic diagram of optimized model structure

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图 7 优化后模型对于工况4预报效果

Figure 7. Prediction performance of the optimized model for condition 4

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表 1 集成模型超参数列表

Table 1. List of integration model superparameters

模型名称	模型超参数
RF回归	CART数量、袋外评分、最大特征数及最大深度等
GBDT回归	CART数量、学习率、阿尔法系数、子样本比例及最大特征数等

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表 2 不同频率区模型预报误差对比

Table 2. Comparison of model prediction errors in different frequency regions

频域/ Hz	RF	GBDT	贝叶斯岭回归
10 ~100	2.49	2.21	2.56
100 ~ 1 000	2.83	2.53	2.76
1 000~10 000	2.12	2.31	3.33

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表 3 不同工况下模型预报误差对比

Table 3. Comparison of model prediction errors under different working conditions

	MAE/dB				RMSE /dB
	工况1	工况2	工况3	工况4	工况1	工况2	工况3	工况4
RF	3.2	3.0	1.2	4.8	3.2	3.1	1.2	4.9
GBDT	2.9	2.9	1.1	4.3	2.9	3.0	1.1	4.4
贝叶斯岭回归	4.3	3.2	1.7	3.8	4.4	3.4	1.8	3.9

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