Incipient fault diagnosis for T–S fuzzy systems with application to high-speed railway traction devices

Incipient fault diagnosis for T–S fuzzy systems with application to high-speed railway traction devices

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This study addresses the problem of incipient fault detection and diagnosis for Takagi–Sugeno (T–S) fuzzy systems and explores further results of total measurable fault information residual (ToMFIR). First, T–S fuzzy model is used to describe the global dynamics of a non-linear system and the model of incipient actuator faults is formalised. Second, based on the ToMFIR, a novel incipient fault detection method is proposed, which removes the assumptions on system structure in some existing work. Further, sliding-mode observers combined with ToMFIR-based thresholds are designed for incipient fault isolation. Finally, application results conducted on a high-speed railway traction device are given to illustrate the effectiveness of the proposed approach.


    1. 1)
      • 1. Ding, S.: ‘Model-based fault diagnosis techniques: design schemes, algorithms, and tools’ (Springer Science & Business Media, 2008).
    2. 2)
      • 2. Chen, J., Patton, R.J.: ‘Robust model-based fault diagnosis for dynamic systems’ (Springer Science & Business Media, 2012).
    3. 3)
      • 3. Hu, Q.L., Xiao, B., Friswell, M.I.: ‘Robust fault tolerant control for spacecraft attitude stabilization subject to input saturation’, IET Control Theory Appl., 2011, 5, pp. 271282.
    4. 4)
      • 4. Jiang, B., Xu, D.Z., Shi, P., et al: ‘Adaptive neural observer-based backstepping fault tolerant control for near space vehicle under control effector damage’, IET Control Theory Appl., 2014, 8, pp. 658666.
    5. 5)
      • 5. Hu, Q.L., Xiao, B., Zhang, Y.M.: ‘Fault-tolerant spacecraft attitude control under loss of actuator effectiveness’, AIAA J. Guid. Control Dyn., 2011, 34, pp. 927932.
    6. 6)
      • 6. Zhang, M., Shen, X., Zhang, Li.T.: ‘Fault tolerant attitude control for cubesats with input saturation based on dynamic adaptive neural network’, Int. J. Innov. Comput., Inf. Control, 2016, 12, pp. 651663.
    7. 7)
      • 7. Demetriou, M.A., Polycarpou, M.M.: ‘Incipient fault diagnosis of dynamical systems using online approximators’, IEEE Trans. Autom. Control, 1998, 43, pp. 16121617.
    8. 8)
      • 8. Zhang, X.D., Polycarpou, M.M., Parisini, T.: ‘A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems’, IEEE Trans. Autom. Control, 2002, 47, pp. 576593.
    9. 9)
      • 9. Zhang, J., Swain, A.K., Nguang, S.K.: ‘Detection and isolation of incipient sensor faults for a class of uncertain non-linear systems’, IET Control Theory Appl., 2012, 6, pp. 18701880.
    10. 10)
      • 10. Chen, W., Chowdhury, F.N.: ‘A synthesized design of sliding-mode and Luenberger observers for early detection of incipient faults’, Int. J. Adapt. Control Signal Process., 2010, 24, pp. 10211035.
    11. 11)
      • 11. Nikoukhah, R., Campbell, S.L., Drake, K.: ‘An active approach for detection of incipient faults’, Int. J. Syst. Sci., 2010, 41, pp. 241257.
    12. 12)
      • 12. Ashari, A.E., Nikoukhah, R., Campbell, S.L.: ‘Effects of feedback on active fault detection’, Automatica, 2012, 48, pp. 866872.
    13. 13)
      • 13. Chen, W., Yeh, C.P., Yang, H.L.: ‘ToMFIR-based fault detection approach in frequency domain’, J. Syst. Eng. Electron., 2011, 22, pp. 3337.
    14. 14)
      • 14. Xu, Y.H., Jiang, J.: ‘Optimal sensor location in closed-loop control systems for fault detection and isolation’. Proc. of American Control Conf., Chicago, Illinois, June 2000, pp. 11951199.
    15. 15)
      • 15. Hsiao, T., Tomizuka, M.: ‘Threshold selection for timely fault detection in feedback control system’. Proc. of American Control Conf., Portland, OR, June 2005, pp. 33033308.
    16. 16)
      • 16. Li, X.J., Yang, G.H.: ‘Adaptive fault detection and isolation approach for actuator stuck faults in closed-loop systems’, Int.J. Control, Autom., Syst., 2012, 10, pp. 830834.
    17. 17)
      • 17. Wu, Y.K., Jiang, B., Lu, N.Y., et al: ‘ToMFIR-based incipient fault detection and estimation for high-speed rail vehicle suspension system’, J. Franklin Inst., 2015, 352, pp. 16721692.
    18. 18)
      • 18. Wu, L., Su, X., Shi, P., et al: ‘Model approximation for discrete-time state-delay systems in the T–S fuzzy framework’, IEEE Trans. Fuzzy Syst., 2011, 19, pp. 366378.
    19. 19)
      • 19. Wu, L., Su, X., Shi, P., et al: ‘A new approach to stability analysis and stabilization of discrete-time T–S fuzzy time-varying delay systems’, IEEE Trans. Syst. Man Cybern. B, Cybern., 2011, 41, pp. 273286.
    20. 20)
      • 20. Ksantini, M., Hammami, M.A., Delmotte, F.: ‘On the global exponential stabilization of Takagi–Sugeno fuzzy uncertain systems’, Int. J. Innov. Comput., Inf. Control, 2015, 11, pp. 281294.
    21. 21)
      • 21. Wang, H., Liu, X., Liu, K., et al: ‘Approximation-based adaptive fuzzy tracking control for a class of non-strict-feedback stochastic nonlinear time-delay systems’, IEEE Trans. Fuzzy Syst., 2015, 23, pp. 17461760.
    22. 22)
      • 22. Chen, B., Lin, C., Liu, X.P., et al: ‘Observer-based adaptive fuzzy control for a class of nonlinear delayed systems’, IEEE Trans. Syst. Man Cybern. A, Syst., 2016, 46, pp. 2736.
    23. 23)
      • 23. Xiang, W., Sun, Y., Zhang, L.H.: ‘Fuzzy adaptive prescribed performance control for a class of uncertain chaotic systems with unknown control gains’, Int. J. Innov. Comput., Inf. Control, 2016, 12, pp. 603613.
    24. 24)
      • 24. Henao, H., Razik, H., Capolino, G.: ‘Analytical approach of the stator current frequency harmonics computation for detection of induction machine rotor faults’, IEEE Trans. Ind. Applicat., 2005, 41, pp. 801807.
    25. 25)
      • 25. Nelson, A., Chow, M.: ‘Characterization of coil faults in an axial flux variable reluctance PM motor’, IEEE Trans. Energy Convers., 2002, 17, pp. 340348.
    26. 26)
      • 26. Kowalski, C., Orlowska, K.: ‘Neural networks application for induction motor faults diagnosis’, Math. Comput. Simulat., 2003, 63, pp. 435448.
    27. 27)
      • 27. Song, Y., Song, Q., Cai, W.: ‘Fault-tolerant adaptive control of high-speed trains under traction/braking failures: a virtual parameter-based approach’, IEEE Trans. Intell. Transp. Syst., 2014, 15, pp. 737748.
    28. 28)
      • 28. Li, L.‘Robust fault detection and diagnosis for permanent magnet synchronous motors’. PhD thesis, The Florida State University, 2006.
    29. 29)
      • 29. Yu, G.‘Observer-based fault detection for induction motors’. PhD thesis, University of Alberta, 2006.
    30. 30)
      • 30. Gao, Z.W., Shi, X.Y., Ding, S.: ‘Fuzzy state/disturbance observer design for T–S fuzzy systems with application to sensor fault estimation’, IEEE Trans. Syst. Man Cybern. B, Cybern., 2008, 38, pp. 875880.
    31. 31)
      • 31. Chiasson, J.: ‘A new approach to dynamic feedback linearization control of an induction motor’, IEEE Trans. Autom. Control, 1998, 43, pp. 391397.
    32. 32)
      • 32. Zhang, S.: ‘CRH2 series high-speed railway vehicle’ (China Railway Publishing, 2008).
    33. 33)
      • 33. Edwards, C., Yan, X.G., Spurgeon, S.K.: ‘On the solvability of the constrained Lyapunov problem’, IEEE Trans. Autom. Control, 2007, 52, pp. 19821987.
    34. 34)
      • 34. Qiu, J., Ding, S., Gao, H., et al: ‘Fuzzy-model-based reliable static output feedback H control of nonlinear hyperbolic PDE systems’, IEEE Trans. Fuzzy Syst., 2016, 24, pp. 388400.
    35. 35)
      • 35. Wei, Y., Qiu, J., Fu, S.: ‘Mode-dependent nonrational output feedback control for continuous-time semi-Markovian jump systems with time-varying delay’, Nonlinear Anal., Hybrid Syst., 2015, 16, pp. 5271.
    36. 36)
      • 36. Qiu, J., Feng, G., Yang, J.: ‘New results on robust energy-to-peak filtering for discrete-time switched polytopic linear systems with time-varying delay’, IET Control Theory Appl., 2008, 2, pp. 795806.

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