Skip to main content
SpringerLink
Log in

Sensor fault diagnosis for a class of time delay uncertain nonlinear systems using neural network

  • Published:
International Journal of Automation and Computing Aims and scope Submit manuscript

Abstract

In this paper, a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network. The sensor fault and the system input uncertainty are assumed to be unknown but bounded. The radial basis function (RBF) neural network is used to approximate the sensor fault. Based on the output of the RBF neural network, the sliding mode observer is presented. Using the Lyapunov method, a criterion for stability is given in terms of matrix inequality. Finally, an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. S. Simani, C. Fantuzzi, S. Beghelli. Diagnosis Techniques for Sensor Faults of Industrial Processes. IEEE Transactions on Control Systems Technology, vol. 8, no. 5, pp. 848–855, 2000.

    Article  Google Scholar 

  2. P. M. Frank. Fault Detection in Dynamic Systems Using Analytical and Knowledge-based Redundancy — A Survey and Some New Results. Automatica, vol. 26, no. 3, pp. 459–474, 1990.

    Article  MATH  Google Scholar 

  3. J. J. Gertler. Survey of Model-based Failure Detection and Isolation in Complex Plants. IEEE Control Systems Magazine, vol. 8, no. 6, pp. 3–11, 1998.

    Article  Google Scholar 

  4. S. S. Ge, H. Fan, T. H. Lee. Adaptive Neural Network Control of Nonlinear Systems with Unknown Time Delays. IEEE Transactions on Automatic Control, vol. 48, no. 11, pp. 2004–2010, 2003.

    Article  Google Scholar 

  5. F. H. Hsiao, J. D. Hwang. Stabilization of Nonlinear Singularly Perturbed Multiple Time-delay Systems by Dither. Journal of Dynamic Systems, Measurement, and Control, vol. 118, no. 1, pp. 176–181, 1996.

    Article  MATH  Google Scholar 

  6. S. K. Nguang. Robust Stabilization of a Class of Time-delay Nonlinear Systems. IEEE Transactions on Automatic Control, vol. 45, no. 4, pp. 756–762, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  7. J. H. Park. On the Design of Observer-based Controller of Linear Neutral Delay-differential Systems. Applied Mathematics and Computation, no. 150, no. 1, pp. 195–202, 2004.

  8. Z. Wang, B. Huang, H. Unbehauen. Robust H ∞ Observer Design of Linear Time-delay Systems with Parametric Uncertainty. Systems & Control Letters, vol. 42, no. 4, pp. 303–312, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  9. S. Raghavan, J. K. Hedrick. Observer Design for a Class of Nonlinear Systems. International Journal of Control, vol. 59, no. 2, pp. 515–528, 1994.

    Article  MATH  MathSciNet  Google Scholar 

  10. Y. Xiong, M. Saif. Sliding Mode Observer for Nonlinear Uncertain Systems. IEEE Transactions on Automatic Control, vol. 46, no. 12, pp. 2012–2017, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  11. H. H. Choi, K. S. Ro. LMI-based Sliding-mode Observer Design Method. IEE Proceedings of Control Theory and Application, vol. 152, no. 1, pp. 113–115, 2005.

    Article  Google Scholar 

  12. B. Jiang, M. Staroswiecki, V. Cocquempot. Fault Estimation in Nonlinear Uncertain Systems Using Robust/Slidingmode Observers. IEE Proceedings of Control Theory and Application, vol. 151, no. 1, pp. 29–37, 2004.

    Article  Google Scholar 

  13. P. T. Chee, E. Christopher. Sliding Mode Observers for Detection and Reconstruction of Sensor Faults. Automatica, vol. 38, no. 10, pp. 1815–1821, 2000.

    Google Scholar 

  14. E. Christopher, S. K. Spurgeon, R. J. Patton. Sliding Mode Observers for Fault Detection and Isolation. Automatica, vol. 36, no. 4, pp. 541–553, 2002.

    Google Scholar 

  15. C. Rodriguez, S. Rementeria, J. I. Martin, A. Lafuente, J. Muguerza, J. Perez. A Modular Neural Network Approach to Fault Diagnosis. IEEE Transactions on Neural Networks, vol. 7, no. 2, pp. 326–340, 1996.

    Article  Google Scholar 

  16. T. Marcu, L. Mirea. Robust Detection and Isolation of Process Faults Using Neural Networks. IEEE Control Systems Magazine, vol. 17, no. 5, pp. 72–79, 1997.

    Article  Google Scholar 

  17. A. Bernieri, M. D’Apuzzo, L. Sansone, M. Savastano. A Neural Network Approach for Identification and Fault Diagnosis on Dynamic Systems. IEEE Transactions on Instrumentation and Measurement, vol. 43, no. 6, pp. 867–873, 1994.

    Article  Google Scholar 

  18. F. Filippetti, G. Franceschini, C. Tassoni. Neural Networks Aided On-line Diagnostics of Induction Motor Rotor Faults. IEEE Transactions on Industry Applications, vol. 31, no. 4, pp. 892–899, 1995.

    Article  Google Scholar 

  19. Y. Maki, K. A. Loparo. A Neural-network Approach to Fault Detection and Diagnosis in Industrial Processes. IEEE Transactions on Control Systems Technology, vol. 5, no. 6, pp. 529–541, 1997.

    Article  Google Scholar 

  20. A. T. Vemuri, M. M. Polycarpou. Neural-network-based Robust Fault Diagnosis in Robotic Systems. IEEE Transactions on Neural Networks, vol. 8, no. 6, pp. 1410–1420, 1997.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, PRC

    Mou Chen, Chang-Sheng Jiang & Qing-Xian Wu

Authors
  1. Mou Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chang-Sheng Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qing-Xian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mou Chen.

Additional information

This work was supported by Natural Science Foundation of Jiangsu Province (No. SBK20082815) and Aeronautical Science Foundation of China (No. 20075152014).

Mou Chen received the B. Sc. degree in material science and engineering at Nanjing University of Aeronautics & Astronautics, Nanjing, PRC, in 1998, the M. Sc. and the Ph.D. degrees in automatical control engineering at Nanjing University of Aeronautics & Astronautics, Nanjing, PRC, in 2004. He is currently an associate professor in Automation College at Nanjing University of Aeronautics & Astronautics, PRC.

His research interests include nonlinear control, artificial intelligence, imagine processing and pattern recognition, and flight control.

Chang-Sheng Jiang received his B. Sc. and M. Sc. degrees in automatic control engineering at Nanjing University of Aeronautics & Astronautics, Nanjing, PRC, in 1964 and 1968, respectively. He is currently a professor in Automation College at Nanjing University of Aeronautics & Astronautics, China.

His research interests include nonlinear control, artificial intelligence, imagine processing and pattern recognition, and flight control.

Qing-Xian Wu received his B. Sc. and M. Sc. degrees in automatical control engineering at Southeast University, PRC, in 1982 and 1985, respectively. He is currently a professor in Automation College at Nanjing University of Aeronautics & Astronautics, PRC.

His research interests include nonlinear control, artificial intelligence, imagine processing and pattern recognition, and flight control.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, M., Jiang, CS. & Wu, QX. Sensor fault diagnosis for a class of time delay uncertain nonlinear systems using neural network. Int. J. Autom. Comput. 5, 401–405 (2008). https://doi.org/10.1007/s11633-008-0401-8

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-008-0401-8

Keywords

Search

Navigation