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
  1. 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] [Google Scholar]
  2. 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] [Google Scholar]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. Tang Dongming. Study on Clustering Algorithm and Its Applications[D]. Chengdu: University of Electronic Science and Technology of China, 2010(in Chinese) [Google Scholar]
  9. 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]
  10. 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] [Google Scholar]
  11. 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] [Google Scholar]
  12. Hoffmann H. Kernel PCA for Novelty Detection[J]. Pattern Recognition, 2007, 40 (3): 863– 874 [Article] [Google Scholar]
  13. Zhang Y, Li S, Teng Y. Dynamic Processes Monitoring Using Recursive Kernel Principal Component Analysis[J]. Chemical Engineering Science, 2012, 72: 78– 86 [Article] [Google Scholar]
  14. 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]
  15. 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] [Google Scholar]
  16. 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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.