| 基于神经网络的水下机器人推进器故障诊断 |
| |
| 引用本文： | 万磊,王建国,姜春萌,孙玉山,何斌,李吉庆.基于神经网络的水下机器人推进器故障诊断[J].中国造船,2011(4). |
| |
| 作者姓名： | 万磊 王建国 姜春萌 孙玉山 何斌 李吉庆 |
| |
| 作者单位： | 哈尔滨工程大学水下机器人技术国防科技重点实验室;中国舰船研究设计中心;武汉科技大学城市学院人文学部; |
| |
| 基金项目： | 中国博士后科学基金资助(20100480964); 国家自然科学基金资助项目(50579007); 国家863计划基金资助项目(2008AA092301) |
| |
| 摘 要： | 为提高水下机器人系统的总体可靠性,开展了推进器故障诊断研究。在三层BP神经网络的基础上,提出了一种改进的递归神经网络并推导了网络的训练算法。利用直航、转艏等试验对网络进行训练,将训练好的网络用于水下机器人运动建模,对比模型的输出与实际传感器测量值来获取残差,通过分析残差特性来提取故障诊断判据,进而进行推进器故障诊断。将提出的方法应用到仿真试验和海上试验中,得出了相应的试验结果。通过对试验结果的分析研究,验证了方法的有效性与可行性,同时也表明该方法在工程应用方面具有一定的参考意义。
|
| 关 键 词： | 水下机器人(UV) 故障诊断 推进器故障 递归神经网络 运动建模 |
Research on Thruster Fault Diagnosis of Underwater Vehicles Based on NN |
| |
| WAN Lei,WANG Jianguo,JIANG Chunmeng,SUN Yushan,HE Bin,LI Jiqing.Research on Thruster Fault Diagnosis of Underwater Vehicles Based on NN[J].Shipbuilding of China,2011(4). |
| |
| Authors: | WAN Lei WANG Jianguo JIANG Chunmeng SUN Yushan HE Bin LI Jiqing |
| |
| Institution: | WAN Lei1,WANG Jianguo2,JIANG Chunmeng3,SUN Yushan1,HE Bin1,LI Jiqing1(1.State Key Laboratory of Autonomous Underwater Vehicle,Harbin Engineering University,Harbin 150001,China,2.China Ship Development and Design Center,Wuhan 430064,3.Humanities Department,City College of Wuhan University of Science and Technology,Wuhan 430083,China) |
| |
| Abstract: | Study of thruster fault diagnosis of underwater vehicles(UVs) is undertaken to improve its whole system reliability.Based on the BP neural network,an improved recurrent neural network(RNN) is presented and the network training algorithm is deduced.The RNN is trained by voyage head and yaw turning experiments,and the well trained network is applied to the model for UVs.Compared the model's outputs with the sensors' outputs,the residuals can be obtained.Fault detection rules are distilled from the residuals t... |
| |
| Keywords: | UVs fault diagnosis thruster fault recurrent neural network(RNN) motion modeling |
| 本文献已被 CNKI 等数据库收录！ |
|
