Development of the algorithm for a comprehensive methodology for assessing the technical condition of a marine propulsion system cylinder piston group based on the indicators of the oil system

E. Mazur, N. L. Velikanov, Grigoriy Evgen'evich Ananev
{"title":"Development of the algorithm for a comprehensive methodology for assessing the technical condition of a marine propulsion system cylinder piston group based on the indicators of the oil system","authors":"E. Mazur, N. L. Velikanov, Grigoriy Evgen'evich Ananev","doi":"10.24143/2073-1574-2024-1-72-83","DOIUrl":null,"url":null,"abstract":"The algorithm of a complex methodology for assessing the technical condition of the cylinder piston group of a marine propulsion system is being investigated. Wear is a continuous process characteristic of all working mechanisms. Studies aimed at identifying factors contributing to the degradation of system elements of devices provide the basis for the development of preventive measures to reduce their effects. Knowledge of the technical condition of marine engine components is important for the development of measures that increase the reliability of equipment and reduce the risks of emergency situations. Some of the main approaches to modeling and evaluating the state of the cylinder-piston system of marine diesel engines are presented. To solve the problems of assessing the technical condition of the cylinder piston group during operation, classical methods of statistical data analysis are considered, methods that artificially increase the size of the data sample are proposed, machine learning methods are analyzed and the most effective for use are determined. An integrated approach is being created to study the operation process of a cylinder-piston group of diesel marine engines based on a combination of statistical methods, machine learning methods and probabilistic forecasting. A diagram of the properties of the studied parameters is illustrated for constructing a model for analyzing a cylinder-piston group system. Machine learning algorithms used to study systems are presented. The proposed technique allows, using the results of indirect measurements (data from lubrication analyses), to determine the technical condition of the engine system, in particular the cylinder piston group.","PeriodicalId":129911,"journal":{"name":"Vestnik of Astrakhan State Technical University. Series: Marine engineering and technologies","volume":"18 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Vestnik of Astrakhan State Technical University. Series: Marine engineering and technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24143/2073-1574-2024-1-72-83","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The algorithm of a complex methodology for assessing the technical condition of the cylinder piston group of a marine propulsion system is being investigated. Wear is a continuous process characteristic of all working mechanisms. Studies aimed at identifying factors contributing to the degradation of system elements of devices provide the basis for the development of preventive measures to reduce their effects. Knowledge of the technical condition of marine engine components is important for the development of measures that increase the reliability of equipment and reduce the risks of emergency situations. Some of the main approaches to modeling and evaluating the state of the cylinder-piston system of marine diesel engines are presented. To solve the problems of assessing the technical condition of the cylinder piston group during operation, classical methods of statistical data analysis are considered, methods that artificially increase the size of the data sample are proposed, machine learning methods are analyzed and the most effective for use are determined. An integrated approach is being created to study the operation process of a cylinder-piston group of diesel marine engines based on a combination of statistical methods, machine learning methods and probabilistic forecasting. A diagram of the properties of the studied parameters is illustrated for constructing a model for analyzing a cylinder-piston group system. Machine learning algorithms used to study systems are presented. The proposed technique allows, using the results of indirect measurements (data from lubrication analyses), to determine the technical condition of the engine system, in particular the cylinder piston group.
基于油系统指标的船舶推进系统气缸活塞组技术状况综合评估方法的算法开发
目前正在研究一种复杂方法的算法,用于评估船舶推进系统气缸活塞组的技术状况。磨损是所有工作机制所特有的一个持续过程。旨在确定导致设备系统元件退化的因素的研究为制定减少其影响的预防措施奠定了基础。了解船用发动机部件的技术状况对于制定提高设备可靠性和降低紧急情况风险的措施非常重要。本文介绍了船用柴油机气缸活塞系统状态建模和评估的一些主要方法。为了解决在运行过程中评估气缸活塞组技术状况的问题,考虑了经典的统计数据分析方法,提出了人为增加数据样本大小的方法,分析了机器学习方法,并确定了最有效的使用方法。基于统计方法、机器学习方法和概率预测的结合,正在创建一种综合方法来研究柴油船用发动机气缸活塞组的运行过程。图中展示了所研究参数的属性,用于构建分析气缸-活塞组系统的模型。介绍了用于研究系统的机器学习算法。所提出的技术可以利用间接测量结果(润滑分析数据)确定发动机系统的技术状况,特别是气缸活塞组。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信