On-line Auxiliary Input Signal Design for Active Fault Detection and Isolation Based on Set-membership and Moving Window Techniques

  • Jing Wang
  • Junde Wang
  • Meng ZhouEmail author


This paper presents an on-line auxiliary input signal design strategy based on set-membership and moving window techniques to deal with the problem of active fault detection and isolation. The goal of active fault detection and isolation is to design an auxiliary input signal, such that the nominal system output set and faulty systems output sets are separated each other after injecting the input signal. In this paper, the output sets are characterized by ellipsoids. First, an extended model of the system based on moving window technique is constructed, then an auxiliary input signal is calculated on-line based on the equivalent model. As the energy of the auxiliary input signal is restricted minimum to decrease the influence of the signal on the system, the design condition of active fault detection and isolation is transformed into an optimal problem. Furthermore, the fault is isolated by judging the actual system output belongs to which output ellipsoid of the faulty models, or determining the probability of the system output is in which faulty model when the output ellipsoids of faulty models are intersecting. Finally, numerical simulations illustrate the feasibility and effectiveness of the proposed approach.


Active fault detection and isolation moving window technique on-line auxiliary input signal design set-membership method 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



  1. [1]
    S. H. Guo, F. L. Zhu, W. Zhang, S. H. Zak and J. Zhang, “ Fault detection and reconstruction for discrete nonlinear systems via Takagi-Sugeno fuzzy models,” International Journal of Control, Automation and Systems, vol. 16, no. 6, pp. 2676–2687, October 2018.CrossRefGoogle Scholar
  2. [2]
    Z. H. Wang, P. Shi, and C. C. Lim, “H-/H fault detection observer in finite frequency domain for linear parameter-varying descriptor systems,” Automatica, vol. 86, pp. 38–45, December 2017.MathSciNetCrossRefGoogle Scholar
  3. [3]
    X. H. Li, C. K. Ahn, D. K. Lu and S. H. Guo, “Robust simultaneous fault estimation and nonfragile output feedback fault-tolerant control for Markovian jump systems,” IEEE Trans. on System Man and Cybern: Systems, 2018. DOI: 10.1109/TSMC.2018.2828123Google Scholar
  4. [4]
    R. Busch and I. K. Peddle, “Active fault detection for open loop stable LTI SISO systems,” International Journal of Control, Automation and Systems, vol. 12, no. 2, pp. 324–332, April 2014.CrossRefGoogle Scholar
  5. [5]
    X. Li, F. Zhu, and J. Zhang, “State estimation and simultaneous unknown input and measurement noise reconstruction based on adaptive H observer,” International Journal of Control, Automation, and Systems, vol. 14, no. 3, pp. 647–654, June 2016.CrossRefGoogle Scholar
  6. [6]
    J. K. Scott, R. Findeisen, R. D. Braatz, and D. M. Raimondo, “Input design for guaranteed fault diagnosis using zonotopes,” Automatica, vol. 50, no. 6, pp. 1580–1589, June 2014.MathSciNetCrossRefGoogle Scholar
  7. [7]
    S. X. Ding, “Data-driven design of monitoring and diagnosis systems for dynamic processes: a review of subspace technique based schemes and some recent results,” Journal of Process Control, vol. 24, no. 2, pp. 431–449, February 2014.CrossRefGoogle Scholar
  8. [8]
    G. R. Marseglia and D. M. Raimondo, “Active fault diagnosis: a multi-parametric approach,” Automatica, vol. 79, pp. 223–230, May 2017.MathSciNetCrossRefGoogle Scholar
  9. [9]
    H. Niemann, “A setup for active fault diagnosis,” IEEE Trans. on Automatic Control, vol. 51, no. 9, pp. 1572–1578, September 2006.MathSciNetCrossRefGoogle Scholar
  10. [10]
    J. Wang, J. Zhang, B. Qu, H. Wu, and J. Zhou, “Unified architecture of active fault detection and partial active fault-tolerant control for incipient faults,” IEEE Trans. on Systems, Man, and Cybernetics: Systems, vol. 47, no. 4, pp. 1688–1700, February 2017.CrossRefGoogle Scholar
  11. [11]
    R. Wang and J. Wang, “Fault-tolerant control with active fault diagnosis for four-wheel independently driven electric ground vehicles,” IEEE Trans. on Vehicular Technology, vol. 60, no. 9, pp. 4276–4287, August 2011.CrossRefGoogle Scholar
  12. [12]
    R. Nikoukhah and S. L. Campbell, “Auxiliary signal design for active failure detection in uncertain linear systems with a priori information,” Automatica, vol. 42, no. 2, pp. 219–228, February 2006.MathSciNetCrossRefGoogle Scholar
  13. [13]
    R. Nikoukhah, S. L. Campbell, and K. Drake, “An active approach for detection of incipient faults,” International Journal of Systems Science, vol. 41, no. 2, pp. 241–257, January 2010.MathSciNetCrossRefGoogle Scholar
  14. [14]
    J. Stoustrup and H. Niemann, “Active fault diagnosis by controller modification,” International Journal of Systems Science, vol. 41, no. 8, pp. 925–936, August 2010.MathSciNetCrossRefGoogle Scholar
  15. [15]
    D. Khandelwal, S. Weiland, and A. Khalate, “Robust fault diagnosis by optimal input design for self-sensing systems,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 1031–1036, March 2017.CrossRefGoogle Scholar
  16. [16]
    J. Skach, I. Puncochar, and O. Straka, “Active fault diagnosis for jump Markov nonlinear systems,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 7308–7313, July 2017.CrossRefGoogle Scholar
  17. [17]
    M. Simandl and I. Puncochar, “Active fault detection and control: unified formulation and optimal design,” Automatica, vol. 45, no. 9, pp. 2052–2059, September 2009.MathSciNetCrossRefGoogle Scholar
  18. [18]
    A. K. Sekunda, H. H. Niemann, and N. K. Poulsen, “Detector design for active fault diagnosis in closed-loop systems,” International Journal of Adaptive Control and Signal Processing, vol. 32, no. 5, pp. 647–664, February 2018.MathSciNetCrossRefGoogle Scholar
  19. [19]
    R. Nikoukha, S. L. Campbell, K. G. Horton, and F. Delebecque, “Auxiliary signal design for robust multimodel identification,” IEEE Trans. on Automatic Control, vol. 47, no. 1, pp. 158–164, August 2002.MathSciNetCrossRefGoogle Scholar
  20. [20]
    A. E. Ashari, R. Nikoukhah, and S. L. Campbell, “Auxiliary signal design for robust active fault detection of linear discrete-time systems,” Automatica, vol. 47, no. 9, pp. 1887–1895, September 2011.MathSciNetCrossRefGoogle Scholar
  21. [21]
    S. Zhai, W. Wang, and H. Ye, “Auxiliary signal design for active fault detection based on set-membership,” IFAC-PapersOnLine, vol. 48, no. 21, pp. 452–457, 2015.CrossRefGoogle Scholar
  22. [22]
    K. Severson, P. Chaiwatanodom, and R. D. Braatz, “Perspectives on process monitoring of industrial systems,” Annual Reviews in Control, vol. 48, no. 21, pp. 931–939, January 2016.Google Scholar
  23. [23]
    L. Ros, A. Sabater, and F. Thomas, “An ellipsoidal calculus based on propagation and fusion,” IEEE Trans. on Systems, Man, and Cybernetics: Cybernetics, vol. 32, no. 4, pp. 430–442, August 2002.CrossRefGoogle Scholar
  24. [24]
    S. L. Campbell, and R. Nikoukhah, “The design of auxiliary signals for robust active failure detection in uncertain systems,” Proc. of the Mathematical Theory of Networks and Systems, 2002.Google Scholar
  25. [25]
    A. E. Ashari, R. Nikoukhah, and S. L. Campbell, “Active robust fault detection in closed-loop systems: quadratic optimization approach,” IEEE Trans. on Automatic Control, vol. 57, no. 10, pp. 2532–2544, October 2012.MathSciNetCrossRefGoogle Scholar

Copyright information

© ICROS, KIEE and Springer 2019

Authors and Affiliations

  1. 1.Beijing University of Chemical TechnologyBeijingChina

Personalised recommendations