船舶动力系统的工作状态可以通过振动、机油检测、温度检测等方法进行识别，但传统的状态识别方法较单一，针对这一问题，本文设计一种基于信息融合技术和SOM神经网络技术的船舶动力系统在线状态评估平台，利用神经网络强大的处理能力，同时将船舶动力系统振动传感器、压力传感器、温度传感器等信号进行分析和处理，最终获取准确的船舶动力系统工作状态。本文重点介绍状态评估系统的整体结构、SOM神经网络的原理、振动信号和功率信号熵特性，最后基于SOM和信息融合技术进行动力系统的状态评估。
The working state of ship power system can be identified by vibration, oil detection, temperature detection and other methods, but the traditional state identification method is relatively simple. To solve this problem, this paper designs an online state evaluation platform of ship power system based on information fusion technology and SOM neural network technology, which uses the powerful processing ability of neural network, and integrates the vibration sensor, pressure sensor, the temperature sensor and other signals are analyzed and processed, and finally the accurate working state of the ship power system is obtained. This paper focuses on the overall structure of the state evaluation system, the principle of SOM neural network, and the entropy characteristics of vibration signals and power signals. Finally, the state evaluation of the dynamic system is carried out based on SOM and information fusion technology.
2022,44(15): 123-126 收稿日期：2022-03-03
DOI：10.3404/j.issn.1672-7649.2022.15.025
分类号：U626.58
基金项目：江苏省高校哲学社会科学研究专题项目（2019SJB609）
作者简介：袁璟瑾(1982-)，女，硕士，助理研究员，研究方向为信息技术及管理
参考文献：
[1] 阮羚, 谢齐家, 高胜友, 等. 人工神经网络和信息融合技术在变压器状态评估中的应用[J]. 高电压技术, 2014, 40(3): 822–828
RUAN Ling, XIE Qi-jia, GAO Sheng-you, et al. Application of artificial neural network and information fusion technology in transformer condition evaluation[J]. High Voltage Technology, 2014, 40(3): 822–828
[2] 杨雪松, 刘勇. 基于BP和SOM神经网络的云平台数据合并技术[J]. 甘肃科技, 2013, 29(9): 24–26
YANG Xue-song, LIU Yong. Cloud platform data merging technology based on BP and SOM neural network[J]. Gansu Science and Technology, 2013, 29(9): 24–26
[3] 赵雨薇, 马波, 刘锦南. 基于RBF神经网络和多信息融合技术的往复压缩机状态评估研究[J]. 压缩机技术, 2013(6): 7–11+15
ZHAO Yu-wei, MA Bo, LIU Jin-nan. Research on condition evaluation of reciprocating compressor based on RBF neural network and multi information fusion technology[J]. Compressor Technology, 2013(6): 7–11+15
[4] 李冬辉, 贾巍等. 基于小波神经网络和数据融合的直流系统故障检测方法及实现[J]. 电力系统保护与控制, 2005, 33(22): 6–9
LI Dong-hui, JIA Wei. DC system fault detection method and implementation based on wavelet neural network and data fusion[J]. Power System Protection and Control, 2005, 33(22): 6–9
[5] 袁英, 郁丰, 踪华, 等. 基于深度BP神经网络的智能信息融合技术[J]. 西北工业大学学报, 2021, 39(S1): 89–95
YUAN Ying, YU Feng, ZONG Hua, et al. Intelligent information fusion technology based on deep BP neural network[J]. Journal of Northwestern Polytechnical University, 2021, 39(S1): 89–95
