Open Access
| Issue |
JNWPU
Volume 38, Number 3, June 2020
|
|
|---|---|---|
| Page(s) | 619 - 626 | |
| DOI | https://doi.org/10.1051/jnwpu/20203830619 | |
| Published online | 06 August 2020 | |
- Abbaspour A, Aboutalebi P, Yen K K, et al. Neural Adaptive Observer-Based Sensor and Actuator Fault Detection in Nonlinear Systems:Application in UAV[J]. ISA Transactions, 2017, 67: 317– 329 [Article] [CrossRef] [Google Scholar]
- Lee J, Shin H, Kim T. Optimal Combination of Fault Detection and Isolation Methods of Integrated Navigation Algorithm for UAV[J]. International Journal of Aeronautical and Space Sciences, 2018, 19 (3): 694– 710 [Article] [CrossRef] [Google Scholar]
- Qiu Zongjiang, Liu Huixia, Xi Qingbiao, et al. UAV PCA Fault Detection and Diagnosis Techniques[J]. Computer Engineering and Applications, 2013, 49 (4): 262– 266 [Article](in Chinese) [Google Scholar]
- Xue Ting, Zhong Maiying, Li Gang. Wavelet Transform and Parity Space Based Actuator Fault Detection for Unmanned Aerial Vehicle[J]. Control Theory & Applications, 2016, 33 (9): 1193– 1199 [Article](in Chinese) [Google Scholar]
- Li Minghu, Li Gang, Zhong Maiying. Application of Dynamic Kernel Principal Component Analysis in Unmanned Aerial Vehicle Fault Diagnosis[J]. Journal of Shandong University, 2017, 47 (5): 215– 222 [Article](in Chinese) [Google Scholar]
- Minjuan Z. Finding Good XML Fragments Based on k-Medoid Cluster Number Optimization and Ranking Model for Feedback[C]//2013 International Conference on Information Technology and Applications, 2013: 333–337 [Google Scholar]
- Guo J, Zhao Y, Li J. A Multi-Relational Hierarchical Clustering Algorithm Based on Shared Nearest Neighbor Similarity[C]//Sixth International Conference on Machine Learning and Cybernetics, 2007: 3951–3955 [Google Scholar]
- Tang Dongming. Study on Clustering Algorithm and Its Applications[D]. Chengdu: University of Electronic Science and Technology of China, 2010(in Chinese) [Google Scholar]
- Zhang Shirong, Cheng Qin, Zhang Fangfang. Fault Detection of Motor Bearings Based on Detection Coils and KPCA Algorithm[J]. Electic Machines & Control Application, 2018, 45 (4): 98– 104 [Article](in Chinese) [Google Scholar]
- Ge Z, Yang C, Song Z. Improved Kernel PCA-Based Monitoring Approach for Nonlinear Processes[J]. Chemical Engineering Science, 2009, 64 (9): 2245– 2255 [Article] [CrossRef] [Google Scholar]
- Jaffel I, Taouali O, Harkat M F, et al. Moving Window KPCA with Reduced Complexity for Nonlinear Dynamic Process Monitoring[J]. ISA Transactions, 2016, 64: 184– 192 [Article] [CrossRef] [Google Scholar]
- Hoffmann H. Kernel PCA for Novelty Detection[J]. Pattern Recognition, 2007, 40 (3): 863– 874 [Article] [CrossRef] [Google Scholar]
- Zhang Y, Li S, Teng Y. Dynamic Processes Monitoring Using Recursive Kernel Principal Component Analysis[J]. Chemical Engineering Science, 2012, 72: 78– 86 [Article] [CrossRef] [Google Scholar]
- Deng Xiaogang, Tian Xuemin. Nonlinear Process Fault Diagnosis Method Using Kernel Principal Component Analysis[J]. Journal of Shandong University, 2005, 35 (3): 103– 106 [Article](in Chinese) [Google Scholar]
- Zhang S, Tang Q, Lin Y, et al. Fault Detection of Feed Water Treatment Process Using PCA-WD with Parameter Optimization[J]. ISA Transactions, 2017, 68: 313[J]. 326 [Article] [CrossRef] [Google Scholar]
- Cen Yigang. The Construction of a Sort of the Scalar(Multi) Wavelet and Wavelet Signal Processing[D]. Wuhan: Huazhong University of Science and Technology, 2006(in Chinese) [Google Scholar]
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