Skip to main content

Weakness Monitors for Fail-Aware Systems

  • Conference paper
  • First Online:
Formal Modeling and Analysis of Timed Systems (FORMATS 2020)

Abstract

Fail-awareness is the ability of a system to detect an upcoming failure before it actually happens. In this paper, we propose a weakness monitoring approach for observing a complex system during its operation, identifying possible degradation of its behavior, and finally raising an alarm in case of an estimated upcoming failure before the system actually goes out of its specification. Our procedure uses online linear regression to monitor trends over time – it is used to optimize the system service. We evaluate our approach on three case studies from the automotive and avionics domains.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Perfectly periodic sampling is not required for our approach, but we use it to simplify the presentation of the procedure.

  2. 2.

    The specification of the sensor defines the safety threshold to be equal to \(2.2^{\circ }\) or \(3^{\circ }\) depending on the model of the sensor. We set this tolerance to a much lower limit for the purpose of evaluation.

  3. 3.

    The parameters of the injected anomaly y(t) have been selected in order to fit it to the short period of time in which the data have been collected. In practice, the anomaly might evolve at a different time scale but the handling approach remains the same.

  4. 4.

    We refer to the cited papers for the definition of STL syntax and semantics.

References

  1. Infineon Technologies, A.G.: TLE 5009 angle sensor: GMR-based angular sensor. Rev. 1.1, 2012–04 (2012)

    Google Scholar 

  2. Babaee, R., Gurfinkel, A., Fischmeister, S.: \(\cal{P}revent\): a predictive run-time verification framework using statistical learning. In: Johnsen, E.B., Schaefer, I. (eds.) SEFM 2018. LNCS, vol. 10886, pp. 205–220. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92970-5_13

    Chapter  Google Scholar 

  3. Bortolussi, L., Cairoli, F., Paoletti, N., Smolka, S.A., Stoller, S.D.: Neural predictive monitoring. In: Finkbeiner, B., Mariani, L. (eds.) RV 2019. LNCS, vol. 11757, pp. 129–147. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32079-9_8

    Chapter  Google Scholar 

  4. Celaya, J., Saxena, A., Saha, S., Goebel, K.F.: Prognostics of power MOSFETs under thermal stress accelerated aging using data-driven and model-based methodologies (2011)

    Google Scholar 

  5. Celaya, J.R., Saxena, A., Saha, S., Vashchenko, V., Goebel, K.: Prognostics of power MOSFET. In: 2011 IEEE 23rd International Symposium on Power Semiconductor Devices and ICs, pp. 160–163. IEEE (2011)

    Google Scholar 

  6. Automotive Electronics Councel: AEC-Q100 rev. h, failure mechanism based stress test qualification for integrated circuits (2014)

    Google Scholar 

  7. Daigle, M., Kulkarni, C.S.: A battery health monitoring framework for planetary rovers. In: 2014 IEEE Aerospace Conference, pp. 1–9. IEEE (2014)

    Google Scholar 

  8. Degrenne, N., Ewanchuk, J., David, E., Boldyrjew, R., Mollov, S.: A review of prognostics and health management for power semiconductor modules. In: Annual Conference of the Prognostics and Health Management Society 2015, vol. 6, pp. 1–9 (2015)

    Google Scholar 

  9. Donzé, A., Ferrère, T., Maler, O.: Efficient robust monitoring for STL. In: Computer Aided Verification (CAV), pp. 264–279 (2013)

    Google Scholar 

  10. Fetzer, C., Cristian, F.: Fail-awareness in timed asynchronous systems. In: Proceedings of the Fifteenth Annual ACM Symposium on Principles of Distributed Computing, pp. 314–321 (1996)

    Google Scholar 

  11. Garcia, C.E., Prett, D.M., Morari, M.: Model predictive control: theory and practice - a survey. Automatica 25(3), 335–348 (1989)

    Article  Google Scholar 

  12. Gouriveau, R., Medjaher, K., Zerhouni, N.: From Prognostics and Health Systems Management to Predictive Maintenance 1: Monitoring and Prognostics. Wiley, Hoboken (2016)

    Book  Google Scholar 

  13. Granig, W., Weinberger, M., Reidl, C., Bresch, M., Strasser, M., Pircher, G.: Integrated gmr angle sensor for electrical commutated motors including features for safety critical applications. Procedia Eng. 5, 1384–1387 (2010)

    Article  Google Scholar 

  14. Gu, J., Vichare, N., Tracy, T., Pecht, M.: Prognostics implementation methods for electronics. In: 2007 Annual Reliability and Maintainability Symposium, pp. 101–106. IEEE (2007)

    Google Scholar 

  15. Haghighi, I., Mehdipour, N., Bartocci, E., Belta, C.: Control from signal temporal logic specifications with smooth cumulative quantitative semantics. In: 58th IEEE Conference on Decision and Control, CDC 2019, Nice, France, 11–13 December 2019, pp. 4361–4366 (2019)

    Google Scholar 

  16. Hess, A., Calvello, G., Frith, P., Engel, S.J., Hoitsma, D.: Challenges, issues, and lessons learned chasing the “big p”: real predictive prognostics part 2. In: 2006 IEEE Aerospace Conference, pp. 1–19. IEEE (2006)

    Google Scholar 

  17. Hong, S., Zhou, Z., Lv, C.: Storage lifetime prognosis of an intermediate frequency (if) amplifier based on physics of failure method. Chem. Eng. Trans. 33, 1117–1122 (2013)

    Google Scholar 

  18. Yang, H., Baraldi, P., Di Maio, F., Zio, E.: A particle filtering and kernel smoothing-based approach for new design component prognostics. Reliability Eng. Syst. Saf. 134, 19–31 (2015)

    Article  Google Scholar 

  19. James, P.A.: Health monitoring of IGBTs in automotive power converter systems. Ph.D. thesis, University of Manchester (2013)

    Google Scholar 

  20. Kalajdzic, K., Bartocci, E., Smolka, S.A., Stoller, S.D., Grosu, R.: Runtime verification with particle filtering. In: Legay, A., Bensalem, S. (eds.) RV 2013. LNCS, vol. 8174, pp. 149–166. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40787-1_9

    Chapter  Google Scholar 

  21. Maler, O., Nickovic, D.: Monitoring temporal properties of continuous signals. In: Lakhnech, Y., Yovine, S. (eds.) FORMATS/FTRTFT -2004. LNCS, vol. 3253, pp. 152–166. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30206-3_12

    Chapter  MATH  Google Scholar 

  22. Maler, O., Ničković, D.: Monitoring properties of analog and mixed-signal circuits. STTT 15(3), 247–268 (2013)

    Article  Google Scholar 

  23. Raman, V., Donzé, A., Maasoumy, M., Murray, R.M., Sangiovanni-Vincentelli, A.L., Seshia, S.A.: Model predictive control with signal temporal logic specifications. In: 53rd IEEE Conference on Decision and Control, CDC 2014, Los Angeles, CA, USA, 15–17 December 2014, pp. 81–87 (2014)

    Google Scholar 

  24. Rezvanizaniani, S.M., Liu, Z., Chen, Y., Lee, J.: Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (ev) safety and mobility. J. Power Sources 256, 110–124 (2014)

    Article  Google Scholar 

  25. Roemer, M.J., Nwadiogbu, E.O., Bloor, G.: Development of diagnostic and prognostic technologies for aerospace health management applications. In: 2001 IEEE Aerospace Conference Proceedings (Cat. No. 01TH8542), vol. 6, pp. 3139–3147. IEEE (2001)

    Google Scholar 

  26. Rychlik, I.: A new definition of the rainflow cycle counting method. Int. J. Fatigue 9(2), 119–121 (1987)

    Article  Google Scholar 

  27. Silipo, R., Ada, I., Winters, P.: Anomaly detection in predictive maintenance. White Paper, KNIME (2018)

    Google Scholar 

  28. Welford, B.P.: Note on a method for calculating corrected sums of squares and products. Technometrics 4(3), 419–420 (1962)

    Article  MathSciNet  Google Scholar 

  29. Wongpiromsarn, T., Topcu, U., Murray, R.M.: Receding horizon temporal logic planning. IEEE Trans. Automat. Contr. 57(11), 2817–2830 (2012)

    Article  MathSciNet  Google Scholar 

  30. Yoon, H., Chou, Y., Chen, X., Frew, E., Sankaranarayanan, S.: Predictive runtime monitoring for linear stochastic systems and applications to geofence enforcement for UAVs. In: Finkbeiner, B., Mariani, L. (eds.) RV 2019. LNCS, vol. 11757, pp. 349–367. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32079-9_20

    Chapter  Google Scholar 

Download references

Acknowledgements

We would like to thank Brno University of Technology for their comments and the measured data used in the motor weakness monitoring use case. The data were measured on a testbench located in the premises of Brno University of Technology, Central European Institute of Technology, with the motor which was developed during ENIAC-JU project MotorBrain (nr. 270693).

We would like to thank also the anonymous reviewers for their comments on the earlier drafts of the paper.

We acknowledge the support of ECSEL project Autodrive (nr. 7953297) and FFG national project IoT4CPS.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dejan Ničković .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Granig, W., Jakšić, S., Lewitschnig, H., Mateis, C., Ničković, D. (2020). Weakness Monitors for Fail-Aware Systems. In: Bertrand, N., Jansen, N. (eds) Formal Modeling and Analysis of Timed Systems. FORMATS 2020. Lecture Notes in Computer Science(), vol 12288. Springer, Cham. https://doi.org/10.1007/978-3-030-57628-8_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-57628-8_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57627-1

  • Online ISBN: 978-3-030-57628-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics