Online learning based underwater robotic thruster fault detection | |
Xu, Gaofei; Guo W(郭威); Zhao Y(赵洋); Zhou Y(周悦); Zhang YL(张吟龙); Liu, Xinyu; Xu GP(徐高朋); Li, Guangwei | |
刊名 | Applied Sciences (Switzerland) |
2021 | |
卷号 | 11期号:8页码:1-29 |
关键词 | Adaptive fault detection Online learning Particle swarm optimization Thruster system Time delay estimation Underwater robotic |
ISSN号 | 2076-3417 |
产权排序 | 2 |
英文摘要 | This paper presents a novel online learning-based fault detection designed for underwater robotic thruster health monitoring. In the fault detection algorithm, we build a mathematical model between the control variable and the propeller speed by fitting collected online work status data to the model. To improve the accuracy of online modeling, a multi-center PSO algorithm with memory ability is utilized to optimize the modeling parameters. Additionally, a model online update mechanism is designed to accommodate the model to the change of thruster work status and sea environment. During the operation, propeller speed of the underwater robot is predicted through the online learning-based model, and the model residuals are used for thruster health monitoring. To avoid false alarm, an adaptive fault detection strategy is established based on model online update mechanism. The proposed method has been extensively evaluated using different underwater robotics, through a sea trial data simulation, a pool test fault detection experiment and a sea trial fault detection experiment. Compared with fixed model-based method, speed prediction MAE of the online learning model is at least 37.9% lower than that of the fixed model. The online learning-based method show no misdiagnosis in experiments, while the fixed model-based method is misdiagnosed. Experimental results show that the proposed method is competitive in terms of accuracy, adaptability, and robustness. |
资助项目 | Hainan Provincial Natural Science Foundation of China[520QN298] ; National Natural Science Foundation of China[61903357] ; Liaoning Provincial Natural Science Foundation of China[2021JH6/10500114] ; Liaoning Provincial Natural Science Foundation of China[2020MS-032] ; China Postdoctoral Science Foundation[2020M672600] ; National Key R&D Program of China[2020YFC1521704] |
WOS关键词 | PARTICLE FILTER ; SENSOR ; MODEL ; ALGORITHM ; DIAGNOSIS ; SYSTEMS |
WOS研究方向 | Chemistry ; Engineering ; Materials Science ; Physics |
语种 | 英语 |
WOS记录号 | WOS:000643985800001 |
资助机构 | Hainan Provincial Natural Science Foundation of China, grant number 520QN298 ; National Natural Science Foundation of China, grant number 61903357 ; Liaoning Provincial Natural Science Foundation of China, rant number 2021JH6/10500114, 2020-MS-032 ; China Postdoctoral Science Foundation, grant number 2020M672600 ; The National Key R&D Program of China, grant number 2020YFC1521704 |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/28795] |
专题 | 沈阳自动化研究所_水下机器人研究室 |
通讯作者 | Guo W(郭威) |
作者单位 | 1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China 2.College of Engineering Science and Technology, Shanghai Ocean University, Shanghai, 201306, China 3.Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya, 572000, China |
推荐引用方式 GB/T 7714 | Xu, Gaofei,Guo W,Zhao Y,et al. Online learning based underwater robotic thruster fault detection[J]. Applied Sciences (Switzerland),2021,11(8):1-29. |
APA | Xu, Gaofei.,Guo W.,Zhao Y.,Zhou Y.,Zhang YL.,...&Li, Guangwei.(2021).Online learning based underwater robotic thruster fault detection.Applied Sciences (Switzerland),11(8),1-29. |
MLA | Xu, Gaofei,et al."Online learning based underwater robotic thruster fault detection".Applied Sciences (Switzerland) 11.8(2021):1-29. |
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