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山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 130-135.doi: 10.6040/j.issn.1672-3961.0.2017.252

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基于SCKF方法的非线性随机动态系统故障诊断方法

李洪阳,何潇*   

  1. 清华大学自动化系, 北京 100084
  • 收稿日期:2017-04-18 出版日期:2017-10-20 发布日期:2017-04-18
  • 通讯作者: 何潇(1982— ),男,河北徐水人,副教授,博士,主要研究方向为鲁棒滤波、故障诊断与容错控制. E-mail: hexiao@tsinghua.edu.cn E-mail:lihongya16@mails.tsinghua.edu.cn
  • 作者简介:李洪阳(1994— ),男,吉林长春人,硕士研究生,主要研究方向为网络化系统故障估计. E-mail: lihongya16@mails.tsinghua.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61473163,61522309,61490701)

A fault detection and estimation scheme for nonlinear stochastic systems based on SCKF

LI Hongyang, HE Xiao*   

  1. Department of Automation, Tsinghua University, Beijing 100084, China
  • Received:2017-04-18 Online:2017-10-20 Published:2017-04-18

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摘要： 基于平方根容积卡尔曼滤波方法(Square root cubature Kalman filter, SCKF),研究一类非线性随机动态系统的故障检测与估计问题。SCKF对解决复杂非线性系统的状态估计问题,具有精度高、稳定性优和计算复杂度低等优点。针对发生执行器故障的非线性随机动态系统,采用SCKF估计系统状态,并根据状态估计结果,利用滑动时间窗口技术设计残差信号,检测故障发生。在检测到故障之后,构造增广系统,实现对执行器故障幅值的估计。通过仿真试验验证了所提出方法的有效性。

关键词: 非线性随机动态系统, 执行器故障, 故障估计, 平方根容积卡尔曼滤波, 故障检测

Abstract: The fault detection and estimation problem was investigated for a class of nonlinear stochastic systems based on the square root cubature Kalman filter(SCKF). To estimate the states of complex nonlinear systems, SCKF has the outstanding characterizations of higher accuracy, better stability and lower computational burden. For nonlinear stochastic systems subject to actuator faults, the states were estimated based on the square root cubature Kalman filter. Moreover, according to the estimation results, a residual was designed by using the moving-horizon technique to detect the actuator fault. The faults were estimated based on a state augmentation method. A simulation experiment was given to verify the effectiveness of the proposed scheme.

Key words: fault detection, fault estimation, nonlinear stochastic systems, actuator faults, SCKF

中图分类号: 

  • TP206+.3
[1] 周东华, 叶银忠. 现代故障诊断与容错控制[M]. 北京:清华大学出版社, 2000.
[2] 周东华, 胡艳艳. 动态系统的故障诊断技术[J]. 自动化学报, 2009, 35(6): 748-758. ZHOU Donghua, HU Yanyan. Fault diagnosis techniques for dynamic systems[J]. Acta Automatica Sinica, 2009, 35(6):748-758.
[3] DING S X. Model-based fault diagnosis techniques[M]. Berlin: Springer Berlin Heidelberg, 2008.
[4] FRANK P M. Fault diagnosis in dynamic system using analytical and knowledgebased redundancy: a survey and some new result[J]. Automatica, 1990, 18(2): 18-22.
[5] ARASARATNAM I, HAYKIN S. Cubature Kalman filters[J]. IEEE Transactions on Automatic Control, 2009, 54(6): 1254-1269.
[6] HE X, WANG Z D, ZHOU D H. Robust H-infinity filtering for time-delay systems with probabilistic sensor faults[J]. IEEE Signal Processing Letters, 2009, 16(5): 442-445.
[7] LIU Y, HE X, WANG Z D, et al. Optimal filtering for networked systems with stochastic sensor gain degradation[J]. Automatica, 2014, 50(5): 1521-1525.
[8] HE X, WANG Z D, WANG X F, et al. Networked strong tracking filtering with multiple packet dropouts: algorithms and applications[J]. IEEE Transactions on Industrial Electronics, 2014, 61(3): 1454-1463.
[9] ANTONIOU C, BEN-AKIVA M, KOUTSOPOULOS H N. Nonlinear Kalman filtering algorithms for on-line calibration of dynamic traffic assignment models[J]. IEEE Transactions on Intelligent Transportation Systems, 2007, 8(4): 661-670.
[10] KIM B, YI K, YOO H J, et al. An IMM/EKF approach for enhanced multitarget state estimation for application to integrated risk management system[J]. Vehicular Technology IEEE Transactions on, 2015, 64(3): 876-889.
[11] KATRINIOK A, ABEL D. Adaptive EKF-based vehicle state estimation with online assessment of local observability[J]. IEEE Transactions on Control Systems Technology, 2016, 24(4): 1368-1381.
[12] ROMANENKO A, CASTRO J A A M. The unscented filter as an alternative to the EKF for nonlinear state estimation: a simulation case study[J]. Computers & Chemical Engineering, 2004, 28(3): 347-355.
[13] LO K L, RATHAMARIT Y. State estimation of a boiler model using the unscented Kalman filter[J]. IET Generation, Transmission & Distribution, 2008, 2(6): 917-931.
[14] JIA B, XIN M, CHENG Y. High-degree cubature Kalman filter[J]. Automatica, 2013, 2(49): 510-518.
[15] JIA B, XIN M. Adaptive cubature Kalman filter with directional uncertainties[J]. IEEE Transactions on Aerospace & Electronic Systems, 2016, 52(3): 1477-1486.
[16] CUI B, CHEN X, TANG X. Improved cubature Kalman filter for GNSS/INS based on transformation of posterior Sigma-points error[J]. IEEE Transactions on Signal Processing, 2017, 65(11): 2975-2987.
[17] ROY A, MITRA D. Multi-target trackers using cubature Kalman filter for Doppler radar tracking in clutter[J]. Iet Signal Processing, 2016, 10(8): 888-901.
[18] 崔乃刚, 张龙, 王小刚,等. 自适应高阶容积卡尔曼滤波在目标跟踪中的应用[J]. 航空学报, 2015, 36(12): 3885-3895. CUI Naigang, ZHANG Long, WANG Xiaogang, et al. The application of adaptive high-degree cubature Kalman filter in target tracking[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(12): 3885-3895
[19] 张秋昭, 张书毕,刘志平,等. 基于奇异值分解的鲁棒容积卡尔曼滤波及其在组合导航中的应用[J]. 控制与决策, 2014(2): 341-346. ZHANG Qiuzhao, ZHANG Shubi, LIU Zhiping, et al. Robust cubature Kalman filter based on SVD and its application to integrated navigation[J]. Control and Decision, 2014(2): 341-346.
[20] CUI B, CHEN X, XU Y, et al. Performance analysis of improved iterated cubature Kalman filter and its application to GNSS/INS[J]. Isa Transactions, 2017: 460-468.
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