船舶柴油机是船舶动力的直接来源，对其进行故障诊断十分重要。本文设计了以ARM S3C2440处理器为核心的嵌入式硬件平台，将专家系统引入到故障诊断中，确定了知识获取途径，提出采用面向对象的知识表示方法，并构建了故障推理和解释机制。同时，在专家系统的基础上进行了系统的功能设计和数据库设计，并将神经网络与专家系统相结合提高知识库的自学习能力，进而提高故障诊断的智能性。
Marine diesel engine is the direct source of marine power, whose fault diagnosis is of great significance. This paper designed an embedded hardware platform based on ARM S3C2440 processor. The way of knowledge acquisition was determined in the system, in which the object-oriented knowledge representation method was described, and the fault reasoning and interpretation mechanism was constructed. At the same time, based on the expert system, the system was sorted and designed, and the function design and database design of the system were completed. In fault diagnosis, neural network and expert system were combined to improve the self-learning ability of knowledge base, which improved the intelligence of fault diagnosis.
2022,44(4): 112-115 收稿日期：2021-08-06
DOI：10.3404/j.issn.1672-7649.2022.04.023
分类号：U665.26
基金项目：浙江省中华职业教育科研项目(ZJCVB22)；浙江省高等教育学会2020年度一般课题(307)
作者简介：王雪峰(1976-)，男，硕士，副教授、轮机长,主要从事船舶安全和防污染研究
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