基于不同置信度训练样本的船舶多支承轴承安装对准方法研究

IF 1.3 4区 工程技术 Q3 ENGINEERING, CIVIL
Yibin Deng, Xiaogang Yang, Shi-dong Fan, Hao Jin, Tao Su, Han-hua Zhu
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引用次数: 0

摘要

由于特种船舶的推进轴系较长,轴承数量较多,而测量到的轴承反力数据较少,给轴系的安装带来了困难。为了将少量的实测数据应用到船舶安装过程中,以便准确计算实际安装中的位移值,本文提出了一种基于不同置信度训练样本的轴系中间轴承位移值计算方法。本研究以一艘滚装船为研究对象,模拟实际安装过程,对少量实测数据给予较高的置信度,构建新的训练样本集进行机器学习,最终得到反映实际安装过程的遗传算法-反向传播(GABP)神经网络。同时,本研究比较了不同置信水平训练样本的轴系神经网络与不含实测数据的轴系神经网络的精度,结果表明,不同置信水平训练样本的轴系神经网络精度更高。虽然随着平差次数和测量数据数量的增加,网络精度得到了显著提高。添加4个实测数据后,最大误差在1%以内,可对船舶推进轴系校中起到指导作用。随着世界科学技术的飞速发展,工程船、公务船、军舰等特种船舶发挥着重要作用(Carrasco et al. 2020;Prill et al. 2020)。这种特殊类型的一些船舶受到诸如机舱尾线、船体稳定性和操作要求等各种因素的限制。它们通常采用中机舱或前机舱的布置,这使得推进系统具有较长的轴,中间轴和中间轴承的数量超过两个。这形成了所谓的多支撑轴系(Lee et al. 2019),由于轴承之间的力耦合,它增加了轴系对准的难度(Lai et al. 2018a, 2018b)。现有的位移值计算方法过程复杂,由于安装误差等因素的影响,需要多次调整轴承高度,使轴承反力满足规范要求(Kim et al. 2017, Ko et al. 2017)。因此,如何准确预测各中间轴承的位移值是解决多支承轴系中间轴承安装标定问题的关键(Zhou et al. 2005, xiaofei et al. 2017)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the Installation and Alignment Method of Ship Multi-support Bearings Based on Different Confidence-Level Training Samples
Because of the long propulsion shafting of special ships, the number of bearings is large and the number of measured bearing reaction data is small, which makes the installation of shafting difficult. To apply a small amount of measured data to the process of ship installation so as to accurately calculate the displacement value in the actual installation, this article proposes a method to calculate the displacement value of shafting intermediate bearing based on different confidence-level training samples. Taking a ro-ro ship as the research object, this research simulates the actual installation process, gives a higher confidence level to a small amount of measured data, constructs a new training sample set for machine learning, and finally obtains the genetic algorithm-backpropagation(GABP) neural network reflecting the actual installation process. At the same time, this research compares the accuracy between different confidence-level training sample shafting neural network and the shafting neural network without measured data, and the results show that the accuracy of shafting neural network with different confidence-level training samples is higher. Although as the adjustment times and the number of measured data increase, the network accuracy is significantly improved. After adding four measured data, the maximum error is within 1%, which can play a guiding role in the ship propulsion shafting alignment. With the rapid development of science and technology in the world, special ships such as engineering ships, official ships, and warships play an important role (Carrasco et al. 2020; Prill et al. 2020). Some ships of this special type are limited by various factors such as the stern line of engine room, hull stability, and operation requirements. They usually adopt the layout of middle or front engine room, which causes the propulsion system to have a longer shaft and the number of intermediate shafts and intermediate bearings exceeds two. This forms a so-called multisupport shafting (Lee et al. 2019) and it increases the difficulty of shafting alignment because of the force-coupling between the bearings (Lai et al. 2018a, 2018b). The process of the existing methods for calculating the displacement value is complex, and because of the influence of installation error and other factors, it is necessary to adjust the bearing height several times to make the bearing reaction meet the specification requirements(Kim et al. 2017, Ko et al. 2017). So how to predict the accurate displacement value of each intermediate bearing is the key to solving the problem of multisupport shafting intermediate bearing installation and calibration (Zhou et al. 2005, Xiao-fei et al. 2017).
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来源期刊
Journal of Ship Research
Journal of Ship Research 工程技术-工程:海洋
CiteScore
2.80
自引率
0.00%
发文量
12
审稿时长
6 months
期刊介绍: Original and Timely technical papers addressing problems of shipyard techniques and production of merchant and naval ships appear in this quarterly publication. Since its inception, the Journal of Ship Production and Design (formerly the Journal of Ship Production) has been a forum for peer-reviewed, professionally edited papers from academic and industry sources. As such, it has influenced the worldwide development of ship production engineering as a fully qualified professional discipline. The expanded scope seeks papers in additional areas, specifically ship design, including design for production, plus other marine technology topics, such as ship operations, shipping economic, and safety. Each issue contains a well-rounded selection of technical papers relevant to marine professionals.
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