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A hybrid feature model and deep learning based fault diagnosis for unmanned aerial vehicle sensors | |
Guo, Dingfei1; Zhong, Maiying2 | |
发表期刊 | Neurocomputing |
2018-11 | |
卷号 | 319期号:2018页码:155-163 |
摘要 | Fault diagnosis plays an important role in guaranteeing system safety and reliability for unmanned aerial vehicles (UAVs). In this study, a hybrid feature model and deep learning based fault diagnosis for UAV sensors is proposed. The residual signals of different sensor faults, including global positioning system (GPS), inertial measurement unit (IMU), air data system (ADS), were collected. This paper used short time fourier transform (STFT) to transform the residual signal to the corresponding time-frequency map. Then, a convolutional neural network (CNN) was used to extract the feature of the map and the fault diagnosis of the UAV sensors was implemented. Finally, the performance of the proposed methodology is evaluated through flight experiments of the UAV. From the visualization, the sensor faults information can be extracted by CNN and the fault diagnosis logic between the residuals and the health status can be constructed successfully. |
关键词 | Model based fault diagnosis Deep learning Short-time fourier transform Convolutional neural network UAV sensors |
学科门类 | 工学 ; 工学::控制科学与工程 |
URL | 查看原文 |
收录类别 | SCI |
语种 | 英语 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47448 |
专题 | 多模态人工智能系统全国重点实验室_仿生进化机器人 |
通讯作者 | Zhong, Maiying |
作者单位 | 1.Institute of Automation Chinese Academy of Sciences 2.Beihang University |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Guo, Dingfei,Zhong, Maiying. A hybrid feature model and deep learning based fault diagnosis for unmanned aerial vehicle sensors[J]. Neurocomputing,2018,319(2018):155-163. |
APA | Guo, Dingfei,&Zhong, Maiying.(2018).A hybrid feature model and deep learning based fault diagnosis for unmanned aerial vehicle sensors.Neurocomputing,319(2018),155-163. |
MLA | Guo, Dingfei,et al."A hybrid feature model and deep learning based fault diagnosis for unmanned aerial vehicle sensors".Neurocomputing 319.2018(2018):155-163. |
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