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- 2017
动态核主元分析在无人机故障诊断中的应用
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Abstract:
摘要： 飞行控制系统作为无人机(unmanned aerial vehicle, UAV)的核心子系统,对其进行故障诊断可以大大提高无人机的安全性和可靠性。在无人机数学模型未知或者不确定的情况下,数据驱动的故障诊断方法比基于模型的方法更实用。考虑无人机飞行控制系统是典型的非线性动态系统,采用一种非线性主元分析方法对其进行故障诊断。利用数据建立无人机飞行控制系统正常状态下的动态核主元模型,通过T2和SPE两种统计量实现故障检测;故障发生后,利用重构贡献图的方法进行故障分离。仿真试验证明,该方法能对典型的无人机执行器和传感器故障进行有效监测和诊断。与动态主元分析相比,动态核主元分析方法对微小故障更为敏感。
Abstract: The flight control system(FCS)was the core subsystem of unmanned aerial vehicle(UAV), performing FD for it could greatly improve the safety and reliability of UAV. When the mathematical model of UAV was unknown or uncertain, data-driven methods were more suitable than model-based FD methods. Considering that FCS of UAV was a typical nonlinear dynamic system, a nonlinear principal component analysis(PCA)method was used instead. A dynamic kernel principal component model under normal state was established for UAV, then fault detection was performed by T2 and SPE statistics; When a fault was detected, a method called reconstruction-based contribution was used for fault isolation. The simulation results showed that the proposed method could achieve better fault diagnosis effect for typical faults of actuators and sensors than linear DPCA model. Besides, DKPCA could achieve high sensitivity for small faults of UAV
| [1] | FAHLSTROM P G, GLEASON T J, 吴汉平, 等. 无人机系统导论[M]. 北京:电子工业出版社, 2003. |
| [2] | MARZAT J, PIET-LAHANIER H, DAMONGEOT F, et al. Model-based fault diagnosis for aerospace systems: a survey[J]. Proceedings of the Institution of Mechanical Engineers: Part G: Journal of Aerospace Engineering, 2012: 226(10):1329-1360. |
| [3] | QIU Z, LIU H, XI Q, et al. UAV PCA fault detection and diagnosis techniques[J]. Computer Engineering and Applications, 2013, 49(4):262-266. |
| [4] | HAGENBLAD A, GUSTAFSSON F, KLEIN I. A comparison of two methods for stochastic fault detection: the parity space approach and principal components analysis[J]. IFAC Proceedings Volumes, 2003, 36(16):1053-1058. |
| [5] | GAO Q, CHANG Y, XIAO Z, et al. Monitoring of distillation column based on indiscernibility dynamic kernel PCA[J]. Mathematical Problems in Engineering, 2016, 2016(4):1-11. |
| [6] | SCHOLKOPF B, SMOLA A, MULLER K R. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural computation, 1998, 10(5):1299-1319. |
| [7] | ALCALA C F, QIN S J. Reconstruction-based contribution for process monitoring with kernel principal component analysis[C] // Proceedings of the 2010 American Control Conference. Baltimore, USA: ACC, 2010: 7022-7027. |
| [8] | LEE J M, YOO C K, CHOI S W, et al. Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science, 2004, 59(1):223-234. |
| [9] | QI X, QI J T, THEILLIOL D, et al. A review on fault diagnosis and fault tolerant control methods for single-rotor aerial vehicles[J]. Journal of Intelligent & Robotic System, 2014, 73(1):535-555. |
| [10] | QIN S J. Survey on data-driven industrial process monitoring and diagnosis[J]. Annual Reviews in Control, 2012, 36(2):220-234. |
| [11] | 白志强. 飞行控制系统故障检测研究与仿真软件开发[D]. 西安:西北工业大学, 2006. BAI Zhiqiang. Fault detection research and simulation software development of flight control system[D]. Xian: Northwestern Polytechnical University, 2006. |
| [12] | JIA M, CHU F, WANG F, et al. On-line batch process monitoring using batch dynamic kernel principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, 2010, 101(2):110-122. |
| [13] | KU W, STORER R H, GEORGAKIS C. Disturbance detection and isolation by dynamic principal component analysis[J]. Chemometrics and intelligent laboratory systems, 1995, 30(1):179-196. |
| [14] | FUJIMAKI R, YAIRI T, MACHIDA K. An approach to spacecraft anomaly detection problem using kernel feature space[C] // Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Chicago, USA:ACM, 2005: 401-410. |
| [15] | CHOI S W, LEE I B. Nonlinear dynamic process monitoring based on dynamic kernel PCA[J]. Chemical engineering science, 2004, 59(24):5897-5908. |
| [16] | VALAVANIS K P, VACHTSEVANOS G J. Handbook of unmanned aerial vehicles[M].[S.l.] : Springer Publishing Company, Incorporated, 2014. |
| [17] | FREEMAN P, PANDITA R, SRIVASTAVA N, et al. Model-based and data-driven fault detection performance for a small UAV[J]. Mechatronics, IEEE/ASME Transactions on, 2013, 18(4):1300-1309. |
| [18] | JAFFEL I, TAUOALI O, HARKAT M F, et al. Moving window KPCA with reduced complexity for nonlinear dynamic process monitoring[J]. ISA transactions, 2016, 64:184-192. |
| [19] | LI M H, LI G, ZHONG M H. A data driven fault detection and isolation scheme for UAV flight control system[C] // Control Conference(CCC), 2016 35th Chinese. Chengdu, China: IEEE Press, 2016: 6778-6783. |
| [20] | GE Z, SONG Z, GAO F. Review of recent research on data-based process monitoring[J]. Industrial & Engineering Chemistry Research, 2013, 52(10):3543-3562. |
