access icon free A class of observer-based fault diagnosis schemes under closed-loop control: performance evaluation and improvement

© The Institution of Engineering and Technology
Received 09/05/2016, Accepted 18/09/2016, Revised 11/08/2016, Published 26/09/2016

This study deals with a fundamental issue of evaluating the performance of widely used fault detection and diagnosis (FDD) schemes within a closed-loop framework. The focus is to examine how certain implemented controller would impact on the FDD performance and how such performance can be further improved. For this purpose, the authors consider the FDD problem for a class of linear discrete-time systems (with and without unknown disturbances) under typical proportional–integral control using observer-based methods. It is revealed that some existing observer-based FDD approaches are no longer applicable in the closed-loop situation due to the feedback control. To solve the problem, by appropriately modifying the structure and re-designing the parameters of the observers, it is proven that the dynamics of closed-loop residuals can be made identical with those of the residuals obtained with known control inputs at each time step. A numerical example is provided to show the effectiveness of the proposed algorithm.

Inspec keywords: PI control; linear systems; observers; control system synthesis; closed loop systems; feedback; discrete time systems

Other keywords: feedback control; performance improvement; proportional-integral control; FDD schemes; closed-loop residuals; linear discrete-time systems; observer-based methods; PI control; performance evaluation; fault detection and diagnosis schemes

Subjects: Linear control systems; Control system analysis and synthesis methods; Discrete control systems; Simulation, modelling and identification

References

    1. 1)
      • 32. Dong, H., Wang, Z., Shen, B., et al: ‘Variance-constrained H∞ control for a class of nonlinear stochastic discrete time-varying systems: the event-triggered design’, Automatica, 2016, 72, pp. 28–36.
    2. 2)
      • 12. Li, S., Wu, R., Gao, Z., et al: ‘Fault service restoration in distribution network based on breadth-first search and improved ant colony algorithm’, J. Shandong Univ. Sci. Technol., 2016, 35, (2), pp. 105–112.
    3. 3)
      • 21. Zeitz, M.: ‘The extended Luenberger observer for nonlinear-systems’, Syst. Control Lett., 1987, 9, (2), pp. 149–156.
    4. 4)
      • 5. Wang, W., Liu, A.C.-F., Chung, H.S.-H., et al: ‘Fault diagnosis of photovoltaic panels using dynamic current-voltage characteristics’, IEEE Trans. Power Electron., 2016, 31, (2), pp. 1588–1599.
    5. 5)
      • 24. Chen, J., Patton, R.J., Zhang, H.Y.: ‘Design of unknown input observers and robust fault-detection filters’, Int. J. Control, 1996, 63, (1), pp. 85–105.
    6. 6)
      • 13. Li, Z., Wang, Z., Ding, D., et al: ‘H∞ fault estimation with randomly occurring uncertainties, quantization effects and successive packet dropouts: the finite-horizon case’, Int. J. Robust Nonlinear Control, 2015, 25, (15), pp. 2671–2686.
    7. 7)
      • 19. Hassanabadia, A.H., Shafieea, M., Puig, V.: ‘UIO design for singular delayed LPV systems with application to actuator fault detection and isolation’, Int. J. Syst. Sci., 2016, 47, (1), pp. 107–121.
    8. 8)
      • 16. Wang, D., Shen, B., Wang, Z., et al: ‘Reliable H∞ filtering for stochastic spatial-temporal systems with sensor saturations and failures’, IET Control Theory Appl., 2015, 9, (17), pp. 2590–2597.
    9. 9)
      • 33. Yang, F., Dong, H., Wang, Z., et al: ‘A new approach to non-fragile state estimation for continuous neural networks with time-delays’, Neurocomputing, 2016, 197, pp. 205–211.
    10. 10)
      • 7. Goktas, T., Arkan, M., Ozguven, O.F.: ‘Detection of rotor fault in three-phase induction motor in case of low-frequency load oscillation’, Electr. Eng., 2015, 97, (4), pp. 337–345.
    11. 11)
      • 14. Liu, Y., Wang, Z., He, X., et al: ‘Filtering and fault detection for nonlinear systems with polynomial approximation’, Automatica, 2015, 54, pp. 348–359.
    12. 12)
      • 15. Dong, H., Wang, Z., Ding, S.X., et al: ‘Finite-horizon estimation of randomly occurring faults for a class of nonlinear time-varying systems’, IEEE Trans. Ind Inf, 2014, 50, (12), pp. 3182–3189.
    13. 13)
      • 18. Li, X., Zhu, F.: ‘Simultaneous time-varying actuator and sensor fault reconstruction based on PI observer for LPV systems’, Int. J. Adaptive Control Signal Process., 2015, 29, (9), pp. 1086–1098.
    14. 14)
      • 1. He, X., Wang, Z., Liu, Y., et al: ‘Least-squares fault detection and diagnosis for networked sensing systems using a direct state estimation approach’, IEEE Trans. Ind. Inf., 2013, 9, (3), pp. 1670–1679.
    15. 15)
      • 10. Liu, P., Yang, L., Gao, Z., et al: ‘Fault tree analysis combined with quantitative analysis for high-speed railway accidents’, Saf. Sci., 2015, 79, pp. 344–357.
    16. 16)
      • 23. Ge, W., Fang, C.-Z.: ‘Detection of faulty components via robust observation’, Int. J. Control, 1988, 47, (2), pp. 581–599.
    17. 17)
      • 28. Qiu, J., Gao, H., Ding, S.X.: ‘Recent advances on fuzzy-model-based nonlinear networked control systems: a survey’, IEEE Trans. Ind. Electron., 2016, 63, (2), pp. 1207–1217.
    18. 18)
      • 34. Luo, Y., Wang, Z., Wei, G., et al: ‘State estimation for a class of artificial neural networks with stochastically corrupted measurements under Round-Robin protocol’, Neural Netw., 2016, 77, pp. 70–79.
    19. 19)
      • 30. Dong, H., Wang, Z., Bu, X., et al: ‘Distributed fault estimation with randomly occurring uncertainties over sensor networks’, Int. J. General Syst., 2016, 45, (5), pp. 662–674.
    20. 20)
      • 17. Ríos, H., Davila, J., Fridman, L., et al: ‘Fault detection and isolation for nonlinear systems via high-order-sliding-mode multiple-observer’, Int. J. Robust Nonlinear Control, 2015, 25, (16), pp. 2871–2893.
    21. 21)
      • 11. Hwang, I., Kim, S., Kim, Y., et al: ‘A survey of fault detection, isolation, reconfiguration methods’, IEEE Trans. Control Syst. Technol., 2010, 18, (3), pp. 636–653.
    22. 22)
      • 9. Chen, J., Jiang, D., Liu, C.: ‘Fault analysis and optimal balancing of bowing of steam turbine rotor under long-term service’, J. Eng. Gas Turbines Power, 2015, 137, (11), pp. 112503-1–112503-10.
    23. 23)
      • 3. Zhang, Y., Wang, Z., Zou, L., et al: ‘Fault detection filter design for networked multi-rate systems with fading measurements and randomly occurring faults’, IET Control Theory Appl., 2016, 10, (5), pp. 573–581.
    24. 24)
      • 35. Hou, N., Dong, H., Wang, Z., et al: ‘Non-fragile state estimation for discrete Markovian jumping neural networks’, Neurocomputing, 2016, 179, pp. 238–245.
    25. 25)
      • 2. Peng, K., Zhang, K., You, B., et al: ‘A quality-based nonlinear fault diagnosis framework focusing on industrial multimode batch processes’, IEEE Trans. Ind. Electron., 2016, 63, (4), pp. 2615–2624.
    26. 26)
      • 6. Wu, W., Lin, T.R., Tan, A.C.C.: ‘Normalization and source separation of acoustic emission signals for condition monitoring and fault detection of multi-cylinder diesel engines’, Mech. Syst. Signal Process., 2015, 64–65, pp. 479–497.
    27. 27)
      • 27. Marx, B., Koenig, D., Ragot, J.: ‘Design of observers for Takagi-Sugeno descriptor systems with unknown inputs and application to fault diagnosis’, IET Control Theory Appl., 2007, 1, (5), pp. 1487–1495.
    28. 28)
      • 4. Zhang, S., Wang, Z., Ding, D., et al: ‘On design of robust fault detection filter in finite-frequency domain with regional pole assignment’, IEEE Trans. Circuits Syst. II, Express Briefs, 2015, 62, (4), pp. 382–386.
    29. 29)
      • 22. Ciccarella, G., Dallamora, M., Germani, A.: ‘A Luenberger-like observer for nonlinear-systems’, Int. J. Control, 1993, 57, (3), pp. 537–556.
    30. 30)
      • 31. Yu, Y., Dong, H., Wang, Z., et al: ‘Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertainties’, Neurocomputing, 2016, 182, pp. 18–24.
    31. 31)
      • 26. Shames, I., Teixeira, A.M.H., Sandberg, H., et al: ‘Distributed fault detection for interconnected second-order systems’, Automatica, 2011, 47, (12), pp. 2757–2764.
    32. 32)
      • 25. Bagherpour, E.A., Hairiyazdi, M.R., Mahjoob, M.J.: ‘Residual generation with unknown input observer for linear systems in the presence of unmatched uncertainties’, J. Mech. Sci. Technol., 2014, 28, (12), pp. 5159–5167.
    33. 33)
      • 20. Luenberger, D.G.: ‘An introduction to observers’, IEEE Trans. Autom. Control, 1971, 16, (6), pp. 596–602.
    34. 34)
      • 8. Ghanbari, T.: ‘Kalman filter based incipient fault detection method for underground cables’, IET Gener. Transm. Distrib., 2015, 9, (14), pp. 1988–1997.
    35. 35)
      • 29. Wang, T., Gao, H., Qiu, J.: ‘A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control’, IEEE Trans. Neural Netw. Learn. Syst., 2016, 27, (2), pp. 416–425.
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