基于卷积神经网络的柴油发电机健康评估

计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 152-154.

基于卷积神经网络的柴油发电机健康评估

赵东明¹, 程焱明¹, 曹明²

武汉理工大学自动化学院武汉430070¹
中国舰船研究设计中心武汉430070²

出版日期:2019-02-26 发布日期:2019-02-26
通讯作者: 程焱明(1994－),男,硕士,主要研究方向为舰船系统健康评估,E-mail:2461954302@qq.com
作者简介:赵东明(1977－),男,博士,副教授,主要研究方向为计算机控制与应用、计算机科学与智能控制,E-mail:502296538@qq.com;程焱明(1994－),男,硕士,主要研究方向为舰船系统健康评估,E-mail:2461954302@qq.com(通信作者);曹明(1987－),男,高级工程师,主要研究方向为舰船系统设计。
基金资助:
本文受国家高技术研究发展计划(2015AA015904)资助。

Health Assessment of Diesel Generator Based on Convolution Neural Network

ZHAO Dong-ming¹, CHENG Yan-ming¹, CAO Ming²

School of Automation,Wuhan University of Technology,Wuhan 430070,China¹
China Ship Development and Design Center,Wuhan 430070,China²

Online:2019-02-26 Published:2019-02-26

摘要/Abstract

摘要： 柴油发电机是水面无人艇(USV)的核心设备之一,其健康状态直接影响USV的航行状态。为了保证USV的健康航行,提出了一种基于卷积神经网络的健康评估方法。该方法以发电机基本参数作为特征参数,建立健康评估模型,得出发电机健康评估状态。以百吨级电力推进USV柴油发电机为实例进行模型验证,得出发电机的健康状态转换关系及健康阈值为0.03。与常用的BP神经网络进行对比,该模型的收敛速度、识别速度、评估准确率都有明显提升。

关键词: 发电机, 健康评估, 卷积神经网络, 无人船

Abstract: Diesel generator is the core equipment of the surface unmanned boat (USV),its health statusdirectly affects the navigation state of USV.In view of the health assessment of diesel generators,a method based on the convolution neural network was proposed.The health assessment model is established by using the basic parameters of the generator as the characteristic parameters,and the state of the motor health assessment is set out.Taking 100 ton electric propulsion USV diesel generator as an example,the model was verified,and the health state transition relationship and the health threshold of the starting motor are 0.03.Compared with the commonly used BP neural network,the convergence speed,recognition speed and accuracy of the model are obviously improved.

Key words: Convolutional neural network, Generator, Health assessment, Unmanned surface vehicle

中图分类号:

TP391

赵东明, 程焱明, 曹明. 基于卷积神经网络的柴油发电机健康评估[J]. 计算机科学, 2018, 45(11A): 152-154. https://doi.org/

ZHAO Dong-ming, CHENG Yan-ming, CAO Ming. Health Assessment of Diesel Generator Based on Convolution Neural Network[J]. Computer Science, 2018, 45(11A): 152-154. https://doi.org/

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