Online Learning Based Underwater Robotic Thruster Fault Detection | |
Xu, Gaofei2; Guo, Wei2; Zhao, Yang3; Zhou, Yue1; Zhang, Yinlong3; Liu, Xinyu2; Xu, Gaopeng3; Li, Guangwei2 | |
刊名 | APPLIED SCIENCES-BASEL |
2021-04-01 | |
卷号 | 11期号:8页码:29 |
关键词 | underwater robotic thruster system time delay estimation particle swarm optimization online learning adaptive fault detection |
DOI | 10.3390/app11083586 |
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 |
出版者 | MDPI |
WOS记录号 | WOS:000643985800001 |
资助机构 | Hainan Provincial Natural Science Foundation of China ; National Natural Science Foundation of China ; Liaoning Provincial Natural Science Foundation of China ; China Postdoctoral Science Foundation ; National Key R&D Program of China |
内容类型 | 期刊论文 |
版本 | 出版稿 |
源URL | [http://ir.idsse.ac.cn/handle/183446/8602] |
专题 | 深海工程技术部_工程实验室 |
通讯作者 | Guo, Wei |
作者单位 | 1.Shanghai Ocean Univ, Coll Engn Sci & Technol, Shanghai 201306, Peoples R China 2.Chinese Acad Sci, Inst Deep Sea Sci & Engn, Sanya 572000, Peoples R China 3.Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang 110016, Peoples R China |
推荐引用方式 GB/T 7714 | Xu, Gaofei,Guo, Wei,Zhao, Yang,et al. Online Learning Based Underwater Robotic Thruster Fault Detection[J]. APPLIED SCIENCES-BASEL,2021,11(8):29. |
APA | Xu, Gaofei.,Guo, Wei.,Zhao, Yang.,Zhou, Yue.,Zhang, Yinlong.,...&Li, Guangwei.(2021).Online Learning Based Underwater Robotic Thruster Fault Detection.APPLIED SCIENCES-BASEL,11(8),29. |
MLA | Xu, Gaofei,et al."Online Learning Based Underwater Robotic Thruster Fault Detection".APPLIED SCIENCES-BASEL 11.8(2021):29. |
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