Data-driven fault diagnosis of FW-UAVs with consideration of multiple operation conditions
文献类型：期刊论文
| 作者 | Shaojun, Liang1; Shirong, Zhang2; Yuping, Huang3; Xing, Zheng1; Jian, Cheng1; Sisi, Wu1 |
| 刊名 | ISA TRANSACTIONS
 |
| 出版日期 | 2022-07-01 |
| 卷号 | 126页码:472-485 |
| 关键词 | Fixed-wing UAV Multiple operation conditions SNN DBSCAN DKPCA |
| ISSN号 | 0019-0578 |
| DOI | 10.1016/j.isatra.2021.07.043 |
| 通讯作者 | Shirong, Zhang(srzhang@whu.edu.cn) |
| 英文摘要 | Fixed-wing Unmanned Aerial Vehicles (FW-UAVs) are intelligent aircrafts. It is of significance to carry out fault diagnosis of FW-UAVs to improve reliability and safety. An entire mission of FW-UAVs contains couple of phases; correspondingly, this paper treats FW-UAVs as multiple operation condition processes. An innovative framework is then proposed for fault diagnosis of FW-UAVs, where the process dynamics, multiple operation conditions, variable data density, and process disturbance are considered. Firstly, augmented matrixes are constructed with the data samples to involve the dynamic characteristic of FW-UAVs. Secondly, a modified DBSCAN algorithm employing Shared Nearest Neighbor based Distance (SNND-DBSCAN) and a K Nearest Neighbor algorithm employing SNND (SNND-KNN) are proposed respectively. They cooperate with each other to realize offline operation condition classification and online recognition. Thirdly, Multiple condition oriented Dynamic KPCA (M-DKPCA) algorithms incorporated with Weighted sliding window denoising (WM-DKPCA) is proposed for fault diagnosis. Finally, the proposed algorithms are tested with real flight data sets in terms of linear and nonlinear faults; and the comparisons between KPCA, DKPCA, M-DKPCA and WM-DKPCA are presented. The results confirm that the multiple condition oriented M-DKPCA and WM-DKPA algorithms are more suitable for fault diagnosis of FW-UAVs; and WSW denoising can indeed improve the fault diagnosis performance. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved. |
| WOS关键词 | PRINCIPAL COMPONENT ANALYSIS ; KERNEL PCA ; MODEL ; SENSOR ; PERFORMANCE ; ALGORITHM ; PARAMETER |
| 资助项目 | National Natural Science Founda- tion of China[51475337] |
| WOS研究方向 | Automation & Control Systems ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000830030800003 |
| 出版者 | ELSEVIER SCIENCE INC |
| 资助机构 | National Natural Science Founda- tion of China |
| 源URL | [http://ir.giec.ac.cn/handle/344007/37069]  |
| 专题 | 中国科学院广州能源研究所 |
| 通讯作者 | Shirong, Zhang |
| 作者单位 | 1.Army Engn Univ, Sch Ordnance Sergeant, Wuhan 430075, Hubei, Peoples R China 2.Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430072, Hubei, Peoples R China 3.Chinese Acad Sci, Guangzhou Inst Energy Convers, Guangzhou 510640, Guangdong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shaojun, Liang,Shirong, Zhang,Yuping, Huang,et al. Data-driven fault diagnosis of FW-UAVs with consideration of multiple operation conditions[J]. ISA TRANSACTIONS,2022,126:472-485. |
| APA | Shaojun, Liang,Shirong, Zhang,Yuping, Huang,Xing, Zheng,Jian, Cheng,&Sisi, Wu.(2022).Data-driven fault diagnosis of FW-UAVs with consideration of multiple operation conditions.ISA TRANSACTIONS,126,472-485. |
| MLA | Shaojun, Liang,et al."Data-driven fault diagnosis of FW-UAVs with consideration of multiple operation conditions".ISA TRANSACTIONS 126(2022):472-485. |
入库方式： OAI收割
来源：广州能源研究所
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