Skip to main content

Advertisement

SpringerLink
Log in

Real Time Diagnosis & Fault Detection for the Reliability Improvement of the Embedded Systems

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

This paper presents the design of a diagnosis procedure in order to improve the reliability of embedded systems subjected to vibration. This procedure is based on the use of wavelet transform of the vibration signals. The transformation provides the wavelet coefficients needed to calculate indicators such as energy and entropy. Artificial neural networks provide a rapid detection of the presence of structural defects. Results have been implemented and verified in real time on a dSPACE platform.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14

Similar content being viewed by others

References

  1. Cooley, J. W., & Tukey, J. W. (1965). An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation, 19(90), 297–301.

    Article  MathSciNet  MATH  Google Scholar 

  2. Rioul, O., & Vetterli, M. (1991). Wavelets and signal processing. IEEE Signal Processing Magazine, 8(4), 14–38.

    Article  Google Scholar 

  3. Morlet, J., Arens, G., Fourgeau, E., & Glard, D. (1982). Wave propagation and sampling theory - Part I: complex signal and scattering in multilayered media. Geophysics, 47(2), 203–221.

    Article  Google Scholar 

  4. Morlet, J., Arens, G., Fourgeau, E., & Glard, D. (1982). Wave propagation and sampling theory - Part II: sampling theory and complex waves. Geophysics, 47(2), 222–236.

    Article  Google Scholar 

  5. Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2(7), 674–693.

    Article  Google Scholar 

  6. Reda Taha, M. M., Nouredin, A., Lucero, J. L., & Baca, T. J. (2006). Wavelet transform for structural health monitoring: a compendium of uses and features. Structural Health Monitoring, 5(3), 267–295.

    Article  Google Scholar 

  7. Doebling, S. W., Farrar, C. R., & Prime, M. B. (1996). Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review. Los Alamos: Los Alamos National Laboratory, New Mexico, USA.

  8. Cifuentes, A. O., Shulga, N., & Neff, C. A. (1995). A sensitivity study of printed wiring boards using a statistical method. Journal of Sound and Vibration, 181(4), 593–604.

    Article  Google Scholar 

  9. Gu, J., & Pecht, M. (2007). Prognostics implementation of electronic under vibration loading. Microelectronics and Reliability, 47, 1849–1856.

    Article  Google Scholar 

  10. Banerjee, S., Ricci, F., Monaco, E., & Mal, A. (2009). A wave propagation and vibration-based approach for damage identification in structural components. Journal of Sound and Vibration, 322, 167–183.

    Article  Google Scholar 

  11. Lefebvre, J. P., Lasaygue, P., Potel, C., De Belleval, J. F., & Gatignol, P. (2004). L’acoustique ultrasonore et ses applications. Acoustique et Techniques, 36, 4–19.

    Google Scholar 

  12. Liu, S., & Ume, C. I. (2004). Defects pattern recognition for flip-chip solder joint quality inspection with laser ultrasound and interferometer. IEEE Transactions on Electronics Packaging Manufacturing, 27(1), 59–66.

    Google Scholar 

  13. Curadelli, R. O., Riera, J. D., Ambrosini, D., & Amani, M. G. (2008). Damage detection by means of structural damping identification. Engineering Structures, 30(12), 3497–3504.

    Google Scholar 

  14. Douka, E., Loutridis, S., & Trochidis, A. (2004). Crack identification in plates using wavelet analysis. Journal of Sound and Vibration, 270, 279–295.

    Article  Google Scholar 

  15. Yam, L. H., Yan, Y. J., & Jiang, J. S. (2003). Vibration-based damage detection for composite structures using wavelet transform and neural network identification. Composite Structures, 60, 403–412.

    Article  Google Scholar 

  16. Bayissa, W. L., Haritos, N., & Thelandersson, S. (2008). Vibration-based structural damage identification using wavelet transform. Mechanical Systems and Signal Processing, 22(5), 1194–1215.

    Google Scholar 

  17. Specht, D. F. (1988). Probabilistic neural networks for classification mapping or associative memory. IEEE International Conference on Neural Networks, 1, 525–532.

    Article  Google Scholar 

  18. Specht, D. F. (1991). A general regression neural network. IEEE Transactions on Neural Networks, 2, 568–576.

    Article  Google Scholar 

  19. Bennouna, O., Chafouk, H., Robin, O., & Roux, J. P. (2010). Embedded diagnosis based on vibration data. International Journal of Adaptive and Innovative Systems, 1(3/4), 285–296.

    Article  Google Scholar 

  20. Bennouna, O., Chafouk, H., Robin, O. & Roux, J. P. (2008). A diagnosis approach combining wavelet transform and artificial neural networks. STA’08: Tunisia.

Download references

Acknowledgments

The work presented in this article was created through funding from the Institute CARNOT ESP. The authors wish to thank everyone who helped us to have this support.

Author information

Authors and Affiliations

  1. IRSEEM (Institut de Recherche en Systèmes Electroniques EMbarqués), EA 4353, Technopôle du Madrillet, Avenue Galilée, BP 10024, 76801, Saint Etienne du Rouvray, France

    O. Bennouna

  2. CEVAA (Centre d’Etudes Vibro-Acoustiques pour l’Automobile), 2 rue Joseph Fourier, 76800, Saint Etienne du Rouvray, France

    J. P. Roux

Authors
  1. O. Bennouna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. P. Roux
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to O. Bennouna.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bennouna, O., Roux, J.P. Real Time Diagnosis & Fault Detection for the Reliability Improvement of the Embedded Systems. J Sign Process Syst 73, 153–160 (2013). https://doi.org/10.1007/s11265-013-0739-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-013-0739-1

Keywords

Search

Navigation