随着信息技术的发展以及智能船舶规范的完善，智能船舶虽未成熟但也得到迅速发展。而智能化最需要数据、算法和计算能力这三方面内容。船舶主机作为动力来源，其状态监测和故障诊断所需的高采样率信号会带来数据量庞大、传输链路带宽不足等问题。因此，设计一种集数据采集、船舶主机信号数据特征分析、网络通讯等功能一体的主机边缘计算设备，并完成了边缘设备与边缘服务平台之间的数据交互和与物联网平台的测试。实验结果表明，船舶主机边缘计算设备可以准确采集数据并进行数据预处理，并通过TCP协议上发，一定程度上减轻了本地及云端计算设备的计算压力，为后续的健康监测或故障诊断等数据分析流程提供了实时准确的数据来源。
With the development of information technology and the improvement of intelligent ship specifications, intelligent ships have developed rapidly although they are not mature. And the most needed aspects for intelligence include data, algorithms, and computing power. As the power source, the high sampling rate signals required for condition monitoring and fault diagnosis of the main engine of the ship will bring problems such as large data volume and insufficient transmission link bandwidth. Therefore, this paper studies and designs a host edge computing device that integrates data acquisition, ship host signal data feature analysis, network communication and other functions, and completes the data interaction between edge device and edge service platform and the test with IoT platform. The experimental results show that the edge computing device of the ship host can accurately collect data and preprocess the data, and send it up through the TCP protocol, which alleviates the computing pressure of local and cloud computing equipment to a certain extent, and provides a real-time and accurate data source for the subsequent data analysis process such as health monitoring or fault diagnosis.
2024,46(15): 152-158 收稿日期：2023-09-12
DOI：10.3404/j.issn.1672-7649.2024.15.027
分类号：U662
基金项目：工信部高技术专项资助项目（工信部联装函［2019］360号）
作者简介：陈智君（1978 – ），男，博士，副教授，研究方向为动力系统建模仿真及控制
参考文献：
[1] 严新平. 智能船舶的研究现状与发展趋势[J]. 交通与港航, 2016(1): 23-26.
[2] 孙旭. 中国智能船舶发展的探索和实践[J]. 中国船检, 2021(3): 11-13.
[3] LI Y. Research status and development trend of intelligent ships[J]. International Core Journal of Engineering, 2019, 5(11): 49-57.
[4] 邱伯华, 陈卓, 黄雅琳, 等. 船舶综合状态评估与健康管理系统研究[J]. 船舶标准化与质量, 2011(6): 41-44.
[5] 余永华, 杨建国, 胡闹. 智能机舱关键部件状态监测诊断技术研究[J]. 船舶, 2018, 29(S1): 98-105.
[6] 翁雨波, 刘碧涛, 桂傲然. 智能船舶发展优势及挑战分析[J]. 船舶标准化与质量, 2019(1): 42-47.
[7] WEISONG SHI, JIE CAO, QUAN ZHANG, et al. Edge computing: vision and challenges[J]. IEEE internet of things journal, 2016, 3(5): 637-646.
[8] 李振. 基于 LoRa 技术的智能船舶监测系统设计与实现[D]. 哈尔滨: 哈尔滨工程大学, 2021.
[9] 胥皓. 船舶结构健康监测边缘计算节点的开发[D]. 大连: 大连理工大学, 2021.
[10] 张文龙. 云/雾/边缘端协同的轴承故障诊断方法研究[D]. 济南: 山东大学, 2020.
[11] 余永华. 船舶柴油机瞬时转速和热力参数监测诊断技术研究[D]. 武汉: 武汉理工大学, 2007.
[12] 张猛, 杨建国, 胡旭钢, 等. 船用中速柴油机嵌入式机旁监控系统研制[J]. 柴油机, 2019, 41(2): 36-41.
[13] 余永华, 沈叶磊, 王勤鹏, 等. 船用低速柴油机缸压闭环控制硬件在环仿真技术研究[J]. 内燃机工程, 2019, 40(5):86-92.
