A fault diagnosis method based on attention mechanism with application in Qianlong-2 autonomous underwater vehicle | |
Xia SX(夏绍轩)1,2,3,4; Zhou XF(周晓锋)2,3,4; Shi HB(史海波)2,3,4; Li S(李帅)1,2,3,4; Xu CH(徐春晖)3,4 | |
刊名 | OCEAN ENGINEERING |
2021 | |
卷号 | 233页码:1-12 |
关键词 | Autonomous underwater vehicle Fault diagnosis Attention mechanism Deep learning |
ISSN号 | 0029-8018 |
产权排序 | 1 |
英文摘要 | This study proposed a fault diagnosis method based on deep learning and attention mechanism for autonomous underwater vehicle (AUV). Firstly, a data attention mechanism is proposed to introduce dynamic weighting coefficients of monitoring variables to realize dynamic decorrelation. Then, the automatic feature engineering is realized by a bi-directional gated recurrent unit (GRU) network to acquire the time dynamic characteristics of monitoring variables. Finally, fault detection is implemented via multi-layer perceptron (MLP). With respect to fault identification, this study embeds a spatial attention mechanism in the fault detection network to capture the semantic relationship between monitoring variables and faults, and fault identification result can be obtained by parsing this semantic relationship. We present a new loss function and training strategy for cooperation between the fault detection and identification tasks. The proposed method is validated on the monitoring data of Qianlong-2 AUV obtained during the mission in the South China Sea, which shows the effectiveness and superiority of the method. |
资助项目 | Nation Key R&D Program of China[2018YFC0308205] |
WOS关键词 | RISK ANALYSIS |
WOS研究方向 | Engineering ; Oceanography |
语种 | 英语 |
WOS记录号 | WOS:000661134200018 |
资助机构 | Nation Key R&D Program of China [2018YFC0308205] |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/29058] |
专题 | 沈阳自动化研究所_数字工厂研究室 |
通讯作者 | Zhou XF(周晓锋) |
作者单位 | 1.University of Chinese Academy of Sciences, Beijing 100049, China 2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China 3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China |
推荐引用方式 GB/T 7714 | Xia SX,Zhou XF,Shi HB,et al. A fault diagnosis method based on attention mechanism with application in Qianlong-2 autonomous underwater vehicle[J]. OCEAN ENGINEERING,2021,233:1-12. |
APA | Xia SX,Zhou XF,Shi HB,Li S,&Xu CH.(2021).A fault diagnosis method based on attention mechanism with application in Qianlong-2 autonomous underwater vehicle.OCEAN ENGINEERING,233,1-12. |
MLA | Xia SX,et al."A fault diagnosis method based on attention mechanism with application in Qianlong-2 autonomous underwater vehicle".OCEAN ENGINEERING 233(2021):1-12. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论