Skip to main content
SpringerLink
Log in

Sensor Degradation Detection in Switched Systems

  • RELIABILITY, STRENGTH, AND WEAR RESISTANCE OF MACHINES AND STRUCTURES
  • Published:
Journal of Machinery Manufacture and Reliability Aims and scope Submit manuscript

Abstract

In this paper, we propose a new sensor error anticipation method applied to switched systems. Once a small performance degradation is detected, the concerned sensor is identified before getting errors. Hybrid bond graph is employed to model the system by taking into account the continuous and discrete parts of the switching system. After modeling, observers are used to estimate the system’s parameters, and their outputs serve as inputs for the control charts, which aim to detect abnormal fluctuations and warn about sensor degradation. The proposed approach improves the system reliability and avoid errors that can causes delays in industrial production systems.

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

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.

Similar content being viewed by others

REFERENCES

  1. Liberzon, D., Switching in Systems and Control, Systems & Control: Foundations & Applications, Boston: Springer, 2003. https://doi.org/10.1007/978-1-4612-0017-8

    Book  MATH  Google Scholar 

  2. Liberzon, D., Switched systems, Handbook of Networked and Embedded Control Systems, Hristu-Varsakelis, D. and Levine, W.S., Eds., Control Engineering, Boston: Birkhäuser, 2005, pp. 559–574. https://doi.org/10.1007/0-8176-4404-0_24

    Book  Google Scholar 

  3. Thirumarimurugan, M., Bagyalakshmi, N., and Paarkavi, P., Comparison of fault detection and isolation methods: A review, 10th Int. Conf. on Intelligent Systems and Control (ISCO), Coimbatore, India, 2016, IEEE, 2016, pp. 1–6. https://doi.org/10.1109/ISCO.2016.7726957

  4. Isermann, R., Model-based fault-detection and diagnosis-status and applications, Annu. Rev. Control, 2005, vol. 29, no. 1, pp. 71–85. https://doi.org/10.1016/j.arcontrol.2004.12.002

    Article  Google Scholar 

  5. Patton, R.J. and Chen, J., A review of parity space approaches to fault diagnosis, IFAC Proc. Vol., 1991, vol. 24, no. 6, pp. 65–81. https://doi.org/10.1016/S1474-6670(17)51124-6

  6. Bachir, S., Tnani, S., and Trigeassou, J.-C., and Champenois, G., Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines, IEEE Trans. Ind. Electron., 2006, vol. 53, no. 3, pp. 963–973. https://doi.org/10.1109/TIE.2006.874258

    Article  Google Scholar 

  7. Abboudi, A., and Belmajdoub,F., A new diagnosis approach of hybrid systems through observers and hybrid automata, Int. J. Autom. Smart Technol., 2020, vol. 10, no. 1. https://doi.org/10.5875/ausmt.v10i1.2169

  8. Simani, S., Fantuzzi, C., and Patton, R.J., Model-based fault diagnosis techniques, Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques, Advances in Industrial Control, London: Springer, 2003, pp. 19–60. https://doi.org/10.1007/978-1-4471-3829-7_2

    Book  Google Scholar 

  9. Ruiz-Carcel, C., and Starr,A., Data-based detection and diagnosis of faults in linear actuators, IEEE Trans. Instrum. Meas., 2018, vol. 67, no. 9, pp. 2035–2047. https://doi.org/10.1109/TIM.2018.2814067

    Article  Google Scholar 

  10. Rosenber, R.C. and Karnopp, D.C., A definition of the bond graph language, J. Dyn. Syst., Meas., Control, 1972, vol. 94, no. 3, pp. 179–182. https://doi.org/10.1115/1.3426586

  11. Roychoudhury, I., Daigle, M.J., Biswas, G., and Koutsoukos, X., Efficient simulation of hybrid systems: A hybrid bond graph approach, Simulation, 2011, vol. 87, no. 6, pp. 467–498. https://doi.org/10.1177/0037549710364478

    Article  Google Scholar 

  12. Mosterman, P.J., Hybrid dynamic systems: A hybrid bond graph modeling paradigm and its application in diagnosis, PhD Thesis, Nashville, Tenn.: Vanderbilt Univ., 1997.

  13. Low, C.B., Wang, D., Arogeti, Sh., and Zhang, J.B., Causality assignment and model approximation for hybrid bond graph: Fault diagnosis perspectives, IEEE Trans. Autom. Sci. Eng., 2010, vol. 7, no. 3, pp. 570–580. https://doi.org/10.1109/TASE.2009.2026731

    Article  Google Scholar 

  14. Oueslati, F.E. and Zanzouri, N., Hybrid dynamical system monitoring based on bond graph, 3th Int. Conf. on Automation, Control, Engineering and Computer Science (ACECS’16), 2016, pp. 332–337.

  15. Abboudi, A. and Belmajdoub, F., Hybrid diagnosis method applied to switched mechatronic systems, J. Eur. Syst., Autom., 2021, vol. 54, no. 5, pp. 683–691. https://doi.org/10.18280/jesa.540503

    Article  Google Scholar 

  16. Kohavi, Z. and Jha, N.K., Switching and Finite Automata Theory, Cambridge Univ. Press, 2009.

    Book  MATH  Google Scholar 

  17. Wong, T. and Cormier, G., Bond graph causality assignment and evolutionary multi-objective optimization, Advances and Innovations in Systems, Computing Sciences and Software Engineering, Elleithy, K., Dordrecht: Springer, 2007, pp. 433–438. https://doi.org/10.1007/978-1-4020-6264-3_75

    Book  Google Scholar 

  18. Roberts, S.W., A comparison of some control chart procedures, Technometrics, 1966, vol. 8, no. 3, pp. 411–430. https://doi.org/10.1080/00401706.1966.10490374

    Article  MathSciNet  Google Scholar 

  19. Nelson, L.S., The Shewhart control chart—Tests for special causes, J. Qual. Technol., 1984, vol. 16, no. 4, pp. 237–239. https://doi.org/10.1080/00224065.1984.11978921

    Article  Google Scholar 

  20. Jensen, W.A., Jones-Farmer, L.A., Champ, C.W., and Woodall, W.H., Effects of parameter estimation on control chart properties: A literature review, J. Qual. Technol., 2006, vol. 38, no. 4, pp. 349–364. https://doi.org/10.1080/00224065.2006.11918623

    Article  Google Scholar 

  21. Chen, G., and Cheng, S.W., Max chart: Combining X-bar chart and S chart, Stat. Sin., 1998, vol. 8, no. 1, pp. 263–271.

    MATH  Google Scholar 

  22. Smith, A.E., X-bar and R control chart interpretation using neural computing, Int. J. Prod. Res., 1994, vol. 32, no. 2, pp. 309–320. https://doi.org/10.1080/00207549408956935

    Article  MATH  Google Scholar 

  23. Abboudi, A., and Belmajdoub, F., Dynamic thresholds for a reliable diagnosis of switched systems, J. Eur. Syst. Autom., 2021, vol. 54, no. 6, pp. 827–833. https://doi.org/10.18280/jesa.540604

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Industrial Techniques (LTI), FST Fez, University Sidi Mohamed Ben Abdellah, 30000, Fez, Morocco

    A. Abboudi & F. Belmajdoub

Authors
  1. A. Abboudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. Belmajdoub
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to A. Abboudi or F. Belmajdoub.

Ethics declarations

The authors declare that they have no conflicts of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abboudi, A., Belmajdoub, F. Sensor Degradation Detection in Switched Systems. J. Mach. Manuf. Reliab. 52, 246–255 (2023). https://doi.org/10.3103/S1052618823030020

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S1052618823030020

Keywords:

Search

Navigation