摘要
近年来,随着无线通信、传感器网络等技术的快速发展,船舶远程监控系统在保障航运安全、维护船载设备等方面发挥着越来越重要的作用。船舶远程监控系统实现船岸间数据信息共享和交互,对船载设备的运行状态进行实时监控,并根据采集的数据进行状态预测和故障诊断。本文在船舶远程监控系统基础上,利用BP神经网络的自组织学习特性,提出远程故障诊断模型,该模型能够根据设备运行参数对故障趋势进行准确判断并发出预警。
In recent years,with the rapid development of wireless communication,sensor networks technologies,the remote monitoring and control system has played a more and more important role in ensuring the safety of navigation, maintenance of ship equipment. The data information sharing and interaction between ship and shore are realized in the remote monitoring system of ship. The operation status of the ship borne equipment is monitored in real time,and the state prediction and fault diagnosis are made according to the collected data. The in the remote monitor system of ship based,using neural network selforganizing learning characteristics proposed remote fault diagnosis model. The model can according to the equipment operating parameters of fault trend of accurate judgment and issued a warning.
出处
《舰船科学技术》
北大核心
2016年第8X期163-165,共3页
Ship Science and Technology
关键词
BP神经网络
远程监控
故障诊断
BP neural networks
remote monitoring
fault diagnosis
