[1]丁 盼.基于软PLC的船用柴油机监控系统设计[J].工业仪表与自动化装置,2026,(02):18-23.[doi:10.19950/j.cnki.CN61-1121/TH.2026.02.004]
　DING Pan.Design of a marine diesel engine monitoring system based on soft PLC[J].Industrial Instrumentation & Automation,2026,(02):18-23.[doi:10.19950/j.cnki.CN61-1121/TH.2026.02.004]
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基于软PLC的船用柴油机监控系统设计()

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2026年02期
页码:
18-23
栏目:
出版日期:
2026-04-15

文章信息/Info

Title:
Design of a marine diesel engine monitoring system based on soft PLC
文章编号:
1000-0682（2026）02-0018-06
作者:
丁 盼
重庆川仪自动化股份有限公司，重庆 401123
Author(s):
DING Pan
（Chongqing Chuanyi Automation Co.,Ltd.,Chongqing ,China)
关键词:
船用柴油机监控; 软PLC; CODESYS; 多总线; 混合学习; 故障预测
Keywords:
marine diesel engine monitoring;  soft PLC;  CODESYS;  multiple fieldbus ;  hybrid algorithm; fault prediction
分类号:
TH17
DOI:
10.19950/j.cnki.CN61-1121/TH.2026.02.004
文献标志码:
A
摘要:
为满足现代船舶对柴油机监控与智能运维的更高要求，该研究设计了一套基于软PLC的船用柴油机监控系统。该系统以树莓派4B为核心，采用模块化设计理念，软件设计结合使用Linux操作系统与CODESYS运行时，硬件设计主控板、安保模块、IO模块、机旁仪表和遥控仪表等多个模块集成。通信方面，主控板通过CAN总线与发动机ECU通信，并利用RS485总线实现了安保模块的冗余通信。系统在功能上实现了数据采集与处理的多重备份、通信故障的自动转移以及故障预测的自学习。故障预测采用自编码机与随机森林的混合学习算法进行故障预测。相比较传统的基于PLC的监控系统，该系统设计具备软硬件开发的灵活性，还具有后续升级的潜力。
Abstract:
The increasing demand for intelligent monitoring and maintenance of modern marine diesel engines necessitates the development of advanced and flexible control systems. This paper presents the design of a novel monitoring system utilizing a soft Programmable Logic Controller (PLC) architecture. The system is centered on a Raspberry Pi 4B, adopting a modular design philosophy. The software architecture integrates the Linux operating system with the CODESYS runtime, while the hardware comprises a main control board, a safety module, an I/O module, and both local and remote instrumentation. Communication with the engine’s Electronic Control Unit (ECU) is established via a CAN bus, with a redundant RS485 bus ensuring reliable data exchange with the safety module. Key system functionalities include multi-level redundancy for data acquisition and processing, automatic communication failover, and a self-learning capability for fault prediction. A hybrid machine learning model, combining an autoencoder with a random forest algorithm, is employed for predictive diagnostics. Compared to conventional PLC-based systems, the proposed design offers superior flexibility in both hardware and software development, presenting a scalable platform with significant potential for future upgrades in intelligent marine engine diagnostics.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2025-09-25第一作者：丁盼（1991—），男，硕士，工程师，研究方向为机电一体化、嵌入式设计。
更新日期/Last Update: 1900-01-01