A great number of pipelines in China are in unsatisfactory condition and faced with problems of corrosion and cracking,but there are very few approaches for underwater pipeline detection.Pipeline detection autonomous ...A great number of pipelines in China are in unsatisfactory condition and faced with problems of corrosion and cracking,but there are very few approaches for underwater pipeline detection.Pipeline detection autonomous underwater vehicle(PDAUV) is hereby designed to solve these problems when working with advanced optical,acoustical and electrical sensors for underwater pipeline detection.PDAUV is a test bed that not only examines the logical rationality of the program,effectiveness of the hardware architecture,accuracy of the software interface protocol as well as the reliability and stability of the control system but also verifies the effectiveness of the control system in tank experiments and sea trials.The motion control system of PDAUV,including both the hardware and software architectures,is introduced in this work.The software module and information flow of the motion control system of PDAUV and a novel neural network-based control(NNC) are also covered.Besides,a real-time identification method based on neural network is used to realize system identification.The tank experiments and sea trials are carried out to verify the feasibility and capability of PDAUV control system to complete underwater pipeline detection task.展开更多
Before the task of autonomous underwater vehicle(AUV) was implemented actually,its semi-physical simulation system of pipeline tracking had been designed.This semi-physical simulation system was used to test the softw...Before the task of autonomous underwater vehicle(AUV) was implemented actually,its semi-physical simulation system of pipeline tracking had been designed.This semi-physical simulation system was used to test the software logic,hardware architecture,data interface and reliability of the control system.To implement this system,the whole system plan,including interface computer and the methods of pipeline tracking,was described.Compared to numerical simulation,the semi-physical simulation was used to test the real software and hardware more veritably.In the semi-physical simulation system,tracking experiments of both straight lines and polygonal lines were carried out,considering the influence of ocean current and the situation of buried pipeline.The experimental results indicate that the AUV can do pipeline tracking task,when angles of pipeline are 15°,30°,45° and 60°.In the ocean current of 2 knots,AUV could track buried pipeline.展开更多
近年来数据挖掘技术的快速发展使得利用水下机器人作业过程中积累的大量数据进行故障诊断成为可能;基于数据挖掘的故障诊断技术能够从数据中获取潜在的诊断知识;针对水下机器人推进器系统数据特征,提出一种基于聚类和距离的离群点检测方...近年来数据挖掘技术的快速发展使得利用水下机器人作业过程中积累的大量数据进行故障诊断成为可能;基于数据挖掘的故障诊断技术能够从数据中获取潜在的诊断知识;针对水下机器人推进器系统数据特征,提出一种基于聚类和距离的离群点检测方法(outlier detection based on dbscan and distance,ODDD);首先,对数据进行粗聚类,然后采用剪枝规则进行离群点检测,来实现故障诊断;仿真实验结果表明算法能够实现水下机器人快速有效的故障检测。展开更多
基金Project(2011AA09A106)supported by the Hi-tech Research and Development Program of ChinaProject(51179035)supported by the National Natural Science Foundation of ChinaProject(2015ZX01041101)supported by Major National Science and Technology of China
文摘A great number of pipelines in China are in unsatisfactory condition and faced with problems of corrosion and cracking,but there are very few approaches for underwater pipeline detection.Pipeline detection autonomous underwater vehicle(PDAUV) is hereby designed to solve these problems when working with advanced optical,acoustical and electrical sensors for underwater pipeline detection.PDAUV is a test bed that not only examines the logical rationality of the program,effectiveness of the hardware architecture,accuracy of the software interface protocol as well as the reliability and stability of the control system but also verifies the effectiveness of the control system in tank experiments and sea trials.The motion control system of PDAUV,including both the hardware and software architectures,is introduced in this work.The software module and information flow of the motion control system of PDAUV and a novel neural network-based control(NNC) are also covered.Besides,a real-time identification method based on neural network is used to realize system identification.The tank experiments and sea trials are carried out to verify the feasibility and capability of PDAUV control system to complete underwater pipeline detection task.
基金Projects(50909025,51179035) supported by the National Natural Science Foundation of ChinaProject(HEUCFZ1003) supported by the Fundamental Research Funds for Central Universities of China
文摘Before the task of autonomous underwater vehicle(AUV) was implemented actually,its semi-physical simulation system of pipeline tracking had been designed.This semi-physical simulation system was used to test the software logic,hardware architecture,data interface and reliability of the control system.To implement this system,the whole system plan,including interface computer and the methods of pipeline tracking,was described.Compared to numerical simulation,the semi-physical simulation was used to test the real software and hardware more veritably.In the semi-physical simulation system,tracking experiments of both straight lines and polygonal lines were carried out,considering the influence of ocean current and the situation of buried pipeline.The experimental results indicate that the AUV can do pipeline tracking task,when angles of pipeline are 15°,30°,45° and 60°.In the ocean current of 2 knots,AUV could track buried pipeline.
文摘近年来数据挖掘技术的快速发展使得利用水下机器人作业过程中积累的大量数据进行故障诊断成为可能;基于数据挖掘的故障诊断技术能够从数据中获取潜在的诊断知识;针对水下机器人推进器系统数据特征,提出一种基于聚类和距离的离群点检测方法(outlier detection based on dbscan and distance,ODDD);首先,对数据进行粗聚类,然后采用剪枝规则进行离群点检测,来实现故障诊断;仿真实验结果表明算法能够实现水下机器人快速有效的故障检测。
