[1]吴爽,焦淑红.船舶结构监测系统的应力预测研究[J].应用科技,2018,45(06):8-11,16.[doi:10.11991/yykj.201803009]
 WU Shuang,JIAO Shuhong.Prediction of stress pattern in the ship structure monitoring system[J].Applied science and technology,2018,45(06):8-11,16.[doi:10.11991/yykj.201803009]
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船舶结构监测系统的应力预测研究(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第45卷
期数:
2018年06期
页码:
8-11,16
栏目:
船舶与海洋工程
出版日期:
2018-11-05

文章信息/Info

Title:
Prediction of stress pattern in the ship structure monitoring system
作者:
吴爽 焦淑红
哈尔滨工程大学 信息与通信工程学院, 黑龙江 哈尔滨 150001
Author(s):
WU Shuang JIAO Shuhong
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
智能化船舶监测传感器非平稳信号互补集合经验模态分解支持向量机预测模型网格搜索法
Keywords:
intelligent shipmonitorsensornon-stationary signalCEEMDSVMprediction modelgrid search method
分类号:
U662.9
DOI:
10.11991/yykj.201803009
文献标志码:
A
摘要:
传统船舶航行决策对于决策的经验过分依赖,辅助决策可以帮助决策者在决策过程中更好地分析问题、评价和制定方案,其中对船舶结构监测系统的预测研究是船舶中辅助决策前提条件,故着重对该部分进行研究。首先,对船舶结构应力监测传感器的选用及布置方案进行讨论;然后,针对船舶结构应力序列非线性、非平稳信号特点,提出了一种互补集合经验模态分解(CEEMD)和通过改进网格搜索法进行参数寻优的支持向量机(SVM)相结合的结构应力预测模型;最后,利用多种测试样本验证预测模型的可靠性和准确性。通过实验验证,提出的结构应力预测模型在不同海况情况下都具有较高的准确性。
Abstract:
Traditionally, the ship navigation decision-making depends too much on the decision-making experience. The aided decision-making can help decision-makers better analyze, evaluate and formulate plans in the decision-making process, the prediction research of ship structure monitoring system is a prerequisite for auxiliary decision-making in ships, so this part is the focus of research. First of all, the choice and layout scheme of the monitoring sensor for ship structure stress was discussed. Then, in view of the nonlinear and non-stationary signal characteristics of ship structure stress sequence, a structure stress prediction model was proposed by combination of the complementary ensemble empirical mode decomposition (CEEMD) and the support vector machine (SVM) that optimizes parameter through improved grid search method. Finally, a variety of test samples were used to verify the reliability and accuracy of the prediction model. Through verification, the proposed structure stress prediction model has high accuracy under different sea conditions.

参考文献/References:

[1] 杨帆. 舰船破舱稳性实时计算方法研究[D]. 大连:大连理工大学, 2006:12.
[2] 沙丹丹. 基于HHT和SVM的砰击检测算法研究[D]. 哈尔滨:哈尔滨工程大学, 2015:3.
[3] HUANG N E, SHEN Zheng, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the royal society A:mathematical, physical and engineering sciences, 1998, 454(1971):903-995.
[4] LEI Yaguo, LIN Jing, HE Zhengjia, et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery[J]. Mechanical systems and signal processing, 2013, 35(1/2):108-126.
[5] WU Zhaohua, HUANG N. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J]. Advances in adaptive data analysis, 2008, 1(1):1-41.
[6] YEH J R, SHIEH J, HUANG N. Complementary ensemble empirical mode decomposition:a novel noise enhanced data analysis method[J]. Advances in adaptive data analysis, 2010, 2(2):135-156.
[7] GUO Guodong, LI S Z. Contents-based audio classification and retrieval by support vector machines[J]. IEEE transactions on neural network, 2003, 14(1):209-215.
[8] 王军栋, 齐维贵. 基于EDM-SVM的江水浊度预测方法研究[J]. 电子学报, 2009(10):2130-2133.
[9] 张金涛. 光纤光栅传感器复用关键技术的研究与应用[D]. 合肥:合肥工业大学, 2015.
[10] 曲健, 陈红岩, 刘文贞, 等. 基于改进网格搜索法的支持向量机在气体定量分析中的应用[J]. 传感技术学报, 2015, 28(5):774-778.
[11] QIAO Meiying, MA Xiaoping, LAN Jianyi, et al. Time-series gas prediction model using LS-SVR within a Bayesian framework[J]. Mining science and technology, 2011, 21(1):153-157.
[12] 吕震宇. 基于ELMD和KNN分类器的船体砰击载荷检测算法研究[D]. 哈尔滨:哈尔滨工程大学, 2016.

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备注/Memo

备注/Memo:
收稿日期:2018-03-16。
基金项目:国家自然科学基金项目(KY10100160075)
作者简介:吴爽(1994-),女,硕士研究生;焦淑红(1966-),女,教授,博士生导师
通讯作者:吴爽,E-mail:ws18845108291@163.com
更新日期/Last Update: 2018-11-02