针对船舶中央冷却水系统日益复杂、处理这类信息时存在很大不确定性的问题，将动态贝叶斯网络运用于复杂管路系统的检测与控制中。通过全面分析某型船舶中央冷却水系统各个部件之间的相互关系以及评估参数，建立系统的动态贝叶斯网络模型，运用BK算法对系统的运行状态进行推理。使目标节点的各个特征因素以及不同时间片同一特征因素相互修正，克服系统检测时不确定性、数据不完整和主观性。通过仿真表明，动态贝叶斯网络在不确定环境和数据缺失的情况下可以考虑时间的因素进行有效的状态推理并实现有效的控制。
In order to solve the problem of the central cooling water system which becomes more and more complex and has great uncertainty, the application of dynamic Bayesian network to the detection and control of the complex pipeline system is used in this paper. Through the comprehensive analysis of the relationship between a certain type of marine central cooling water system components and evaluation parameters, the dynamic Bayesian network model is established and the BK algorithm is used for inference. Each characteristic factor of the target node and the same characteristic factor of different time slice are corrected to overcome the uncertainty, incomplete data and subjectivity. The simulation results show that the dynamic Bayesian network can take the time factor into consideration in the case of uncertain environment and incomplete data, and can effectively control state.
2016,38(12): 104-109 收稿日期：2016-04-20
DOI：10.3404/j.issn.1672-7619.2016.12.021
分类号：U664.84
基金项目：中国博士后科学研究基金资助项目（201150M1547）
作者简介：孟瑞(1991-),男,硕士研究生,研究方向为舰船动力装置自动化与仿真技术。
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