Fault Diagnosis of Underwater Robots Based on Recurrent Neural Network

摘要：

Research on thruster fault diagnosis of Underwater Robots (URs) is undertaken to improve its whole system reliability. Based on the BP neural network, a 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 model for the URs. Compared the outputs between model and sensor, the residuals can be acquired; Fault diagnosis rules can be reached from the residuals to execute thruster fault detection. The methods proposed here are used for the simulation experiments and sea trials, and plenty of results are obtained. Based on the analysis of the experiment results, the validity and feasibility of the methods can be verified, and some guidance value in practical engineering applications can be demonstrated by the results.

关键词： Underwater Robot fault diagnosis recurrent neural network (RNN) thruster fault motion modeling

作者: Jianguo WANG Gongxing WU Yushan SUN Lei WAN Dapeng JIANG

作者单位: State Key Laboratory of Autonomous Underwater Vehicle Harbin Engineering University Harbin,Heilongjiang Province 150001,China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)

会议地点: 桂林

会议语种:英文

页码: 2497-2502

在线出版日期: 2009-12-19（万方平台首次上网日期，不代表论文的发表时间）