Skip to main content

Diagnosis in Cyber-Physical Systems with Fault Protection Assemblies

  • Chapter
  • First Online:
Book cover Diagnosability, Security and Safety of Hybrid Dynamic and Cyber-Physical Systems

Abstract

Fault Protection Assemblies are used in cyber-physical systems for automated fault-isolation. These devices alter the mode of the system using locally available information in order to stop fault propagation. For example, in electrical networks relays and breakers isolate faults in order to arrest failure propagation and protect the healthy parts of the system. However, these assemblies themselves can have faults, which may inadvertently induce secondary failures. Often these secondary failures lead to cascade effects, which then lead to total system collapse. This behavior is often seen in electrical transmission systems where failures of relays and breakers may cause overloading and the disconnection of parts of an otherwise healthy system. In the past, we had developed a consistency based diagnosis approach for physical systems based on the temporal failure propagation graph. We now describe an extension that uses the concept of timed discrete event observers in combination with the timed failure propagation graphs to extend the hypothesis to include the possibility of failures in the fault protection units. Using a simulated power system case study, we show that the combined approach is able to diagnose faults in both the plant and the protection devices.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    The extension includes sets of failure modes and failure mode guards.

  2. 2.

    In order to relate to the alarms generated by observers with the failure graph few modifications are performed. The alarms signaled by relays are replaced by their corresponding observers.

References

  1. North American Electric Reliability Corporation, 2012 state of reliability, Tech. Rep. (2012). Available: http://www.nerc.com/files/2012_sor.pdf

  2. S. Abdelwahed, G. Karsai, G. Biswas, A consistency-based robust diagnosis approach for temporal causal systems, in The 16th International Workshop on Principles of Diagnosis (2005), pp. 73–79

    Google Scholar 

  3. N. Mahadevan, A. Dubey, G. Karsai, Application of software health management techniques, in Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-managing Systems, ser. SEAMS ’11 (ACM, New York, 2011), pp. 1–10. Available: http://doi.acm.org/10.1145/1988008.1988010

  4. P. Seifried, Fault detection and diagnosis in chemical and petrochemical processes, Bd. 8 der Serie ,,Chemical Engineering Monographs”. Von D. M. Himmelblau, herausgegeben von S. W. Churchill, Elsevier Scientific Publishing Company, Amsterdam – New York 1978. 1. Aufl., X, 414 S., 137 Abb., 66 Tab., DM 145,–. Chem. Ing. Tech. 51, 766 (1979). https://doi.org/10.1002/cite.330510726

  5. N. Viswanadham, T.L. Johnson, Fault detection and diagnosis of automated manufacturing systems, in 27th IEEE Conference on Decision and Control (1988)

    Google Scholar 

  6. R. Hessian, B. Salter, E. Goodwin, Fault-tree analysis for system design, development, modification, and verification. IEEE Trans. Reliab. 39(1), 87–91 (1990)

    Article  Google Scholar 

  7. Y. Ishida, N. Adachi, H. Tokumaru, Topological approach to failure diagnosis of large-scale systems. IEEE Trans. Syst. Man Cybern. 15(3), 327–333 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  8. S.V.N. Rao, N. Viswanadham, Fault diagnosis in dynamical systems: a graph theoretic approach. Int. J. Syst. Sci. 18(4), 687–695 (1987)

    Article  MATH  Google Scholar 

  9. S.V.N. Rao, N. Viswanadham, A methodology for knowledge acquisition and reasoning in failure analysis of systems. IEEE Trans. Syst. Man Cybern. 17(2), 274–288 (1987)

    Article  Google Scholar 

  10. J. Richman, K.R. Bowden, The modern fault dictionary, in International Test Conference (1985), pp. 696–702

    Google Scholar 

  11. W.T. Scherer, C.C. White, A survey of expert systems for equipment maintenance and diagnostics, in Knowledge-Based System Diagnosis, Supervision and Control, ed. by S.G. Tzafestas (Plenum, New York, 1989), pp. 285–300

    Chapter  Google Scholar 

  12. S. Tzafestas, K. Watanabe, Modern approaches to system/sensor fault detection and diagnosis. J. A. IRCU Lab. 31(4), 42–57 (1990)

    Google Scholar 

  13. P. Frank, Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy – a survey and some new results. Automatica 26(3), 459–474 (1990)

    Article  MATH  Google Scholar 

  14. W. Hamscher, L. Console, J. de Kleer, Readings in Model-Based Diagnosis (Morgan Kaufmann Publishers Inc., San Francisco, 1992)

    Google Scholar 

  15. R. Patton, Robust model-based fault diagnosis: the state of the art, in IFAC Fault Detection, Supervision and Safety for Technical Processes, Espoo (1994), pp. 1–24

    Google Scholar 

  16. R. Patton, P. Frank, R. Clark, Fault Diagnosis in Dynamic Systems: Theory and Application (Prentice Hall International, Englewood Cliffs, 1989)

    Google Scholar 

  17. M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, D. Teneketzis, Failure diagnosis using discrete event models. IEEE Trans. Control Syst. Technol. 4, 105–124 (1996)

    Article  MATH  Google Scholar 

  18. A.N. Srivastava, Discovering system health anomalies using data mining techniques, in Proceedings of the Joint Army Navy NASA Air Force Conference on Propulsion (2005)

    Google Scholar 

  19. R. Reiter, A theory of diagnosis from first principles. Artif. Intell. 32(1), 57–95 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  20. J. de Kleer, A. Mackworth, R. Reiter, Characterizing diagnoses and systems. Artif. Intell. 56, 197–222 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  21. A. Darwiche, Model-based diagnosis using structured system descriptions. J. Artif. Intell. Res. 8, 165–222 (1998)

    MathSciNet  MATH  Google Scholar 

  22. A. Darwiche, G. Provan, Exploiting system structure in model-based diagnosis of discrete-event systems, in Proceedings of the Seventh International Workshop on Principles of Diagnosis (1996), pp. 95–105

    Google Scholar 

  23. J. Gamper, A temporal reasoning and abstraction framework for model-based diagnosis systems. Ph.D. dissertation, RWTH, Aachen, 1996

    Google Scholar 

  24. L. Console, P. Torasso, On the co-operation between abductive and temporal reasoning in medical diagnosis. Artif. Intell. Med. 3(6), 291–311 (1991)

    Article  Google Scholar 

  25. A. Misra, Sensor-based diagnosis of dynamical systems. Ph.D. dissertation, Vanderbilt University, 1994

    Google Scholar 

  26. A. Misra, J. Sztipanovits, J. Carnes, Robust diagnostics: structural redundancy approach, in SPIE’s Symposium on Intelligent Systems (1994)

    Google Scholar 

  27. S. Padalkar, J. Sztipanovits, G. Karsai, N. Miyasaka, K.C. Okuda, Real-time fault diagnostics. IEEE Expert 6(3), 75–85 (1991)

    Article  Google Scholar 

  28. G. Karsai, J. Sztipanovits, S. Padalkar, C. Biegl, Model based intelligent process control for cogenerator plants. J. Parallel Distrib. Syst. 15, 90–103 (1992)

    Article  Google Scholar 

  29. P.J. Mosterman, G. Biswas, Diagnosis of continuous valued systems in transient operating regions. IEEE Trans. Syst. Man Cybern. 29(6), 554–565 (1999)

    Article  Google Scholar 

  30. G. Karsai, G. Biswas, S. Abdelwahed, Towards fault-adaptive control of complex dynamic systems, in Software-Enabled Control: Information Technology for Dynamical Systems, ch. 17, ed. by T. Samad, G. Balas (IEEE Publication, Piscataway, 2003)

    Google Scholar 

  31. S. Abdelwahed, G. Karsai, G. Biswas, System diagnosis using hybrid failure propagation graphs, in The 15th International Workshop on Principles of Diagnosis, Carcassonne, 2004

    Google Scholar 

  32. V. Brusoni, L. Console, P. Terenziani, D.T. Dupre, A spectrum of definitions for temporal model-based diagnosis. Artif. Intell. 102(1), 39–79 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  33. Z. Yongli, Y.H. Yang, B.W. Hogg, W.Q. Zhang, S. Gao, An expert system for power systems fault analysis. IEEE Trans. Power Syst. 9(1), 503–509 (1994)

    Article  Google Scholar 

  34. Y.-C. Huang, Fault section estimation in power systems using a novel decision support system. IEEE Trans. Power Syst. 17(2), 439–444 (2002)

    Article  Google Scholar 

  35. G. Cardoso, J.G. Rolim, H.H. Zurn, Identifying the primary fault section after contingencies in bulk power systems. IEEE Trans. Power Deliv. 23(3), 1335–1342 (2008)

    Article  Google Scholar 

  36. J. Jung, C.-C. Liu, M. Hong, M. Gallanti, G. Tornielli, Multiple hypotheses and their credibility in on-line fault diagnosis. IEEE Trans. Power Deliv. 16(2), 225–230 (2001)

    Article  Google Scholar 

  37. G. Cardoso, J.G. Rolim, H.H. Zurn, Application of neural-network modules to electric power system fault section estimation. IEEE Trans. Power Delivery 19(3), 1034–1041 (2004)

    Article  Google Scholar 

  38. R.N. Mahanty, P.B.D. Gupta, Application of RBF neural network to fault classification and location in transmission lines. IEE Proc. Gener. Transm. Distrib. 151(2), 201–212 (2004)

    Article  Google Scholar 

  39. D. Thukaram, H.P. Khincha, H.P. Vijaynarasimha, Artificial neural network and support vector machine approach for locating faults in radial distribution systems. IEEE Trans. Power Delivery 20(2), 710–721 (2005)

    Article  Google Scholar 

  40. T. Bi, Z. Yan, F. Wen, Y. Ni, C. Shen, F.F. Wu, Q. Yang, On-line fault section estimation in power systems with radial basis function neural network. Int. J. Electr. Power Energy Syst. 24(4), 321–328 (2002)

    Article  Google Scholar 

  41. Y.-X. Wu, X.N. Lin, S.H. Miao, P. Liu, D.Q. Wang, D.B. Chen, Application of family eugenics based evolution algorithms to electric power system fault section estimation, in Transmission and Distribution Conference and Exhibition: Asia and Pacific, 2005 IEEE/PES (2005), pp. 1–5

    Google Scholar 

  42. F. Wen, C. Chang, Probabilistic approach for fault-section estimation in power systems based on a refined genetic algorithm. IEE Proc. Gener. Transm. Distrib. 144(2), 160–168 (1997)

    Article  MathSciNet  Google Scholar 

  43. Z. He, H.-D. Chiang, C. Li, Q. Zeng, Fault-section estimation in power systems based on improved optimization model and binary particle swarm optimization, in IEEE Power & Energy Society General Meeting, 2009. PES’09 (IEEE, Piscataway, 2009), pp. 1–8

    Google Scholar 

  44. W. Guo, F. Wen, G. Ledwich, Z. Liao, X. He, J. Liang, An analytic model for fault diagnosis in power systems considering malfunctions of protective relays and circuit breakers. IEEE Trans. Power Deliv. 25(3), 1393–1401 (2010)

    Article  Google Scholar 

  45. J. Sun, S.-Y. Qin, Y.-H. Song, Fault diagnosis of electric power systems based on fuzzy petri nets, IEEE Trans. Power Syst. 19(4), 2053–2059 (2004)

    Article  Google Scholar 

  46. W.-H. Chen, C.-W. Liu, M.-S. Tsai, Fast fault section estimation in distribution substations using matrix-based cause-effect networks. IEEE Trans. Power Deliv. 16(4), 522–527 (2001)

    Article  Google Scholar 

  47. W.H. Chen, S.H. Tsai, H.I. Lin, Fault section estimation for power networks using logic cause-effect models. IEEE Trans. Power Deliv. 26(2), 963–971 (2011)

    Article  Google Scholar 

  48. W. Guo, L. Wei, F. Wen, Z. Liao, J. Liang, C.L. Tseng, An on-line intelligent alarm analyzer for power systems based on temporal constraint network, in International Conference on Sustainable Power Generation and Supply, 2009. SUPERGEN ’09 (2009), pp. 1–7

    Google Scholar 

  49. W.H. Chen, Online fault diagnosis for power transmission networks using fuzzy digraph models. IEEE Trans. Power Deliv. 27(2), 688–698 (2012)

    Article  Google Scholar 

  50. Z. Yongli, H. Limin, L. Jinling, Bayesian networks-based approach for power systems fault diagnosis. IEEE Trans. Power Deliv. 21(2), 634–639 (2006)

    Article  Google Scholar 

  51. Y. Sekine, Y. Akimoto, M. Kunugi, C. Fukui, S. Fukui, Fault diagnosis of power systems. Proc. IEEE 80(5), 673–683 (1992)

    Article  Google Scholar 

  52. 1962. Available: http://www2.ee.washington.edu/research/pstca/pf14/pg_tca14bus.htm

  53. 1962. Available: http://icseg.iti.illinois.edu/ieee-14-bus-system/

  54. 2016. Available: http://www2.ee.washington.edu/research/pstca/formats/cdf.txt

  55. R. Dugan, OpenDSS Manual. Electrical Power Research Institute, 2016. Available: http://sourceforge.net/apps/mediawiki/electricdss/index.php

    Google Scholar 

  56. S. Padalkar, G. Karsai, C. Biegl, J. Sztipanovits, K. Okuda, N. Miyasaka, Real-time fault diagnostics. IEEE Expert 6(3), 75–85 (1991)

    Article  Google Scholar 

  57. S. Abdelwahed, G. Karsai, Notions of diagnosability for timed failure propagation graphs, in 2006 IEEE Autotestcon, Sept 2006, pp. 643–648

    Google Scholar 

  58. A. Dubey, G. Karsai, N. Mahadevan, Model-based software health management for real-time systems, in 2011 IEEE Aerospace Conference (IEEE, Piscataway, 2011), pp. 1–18

    Google Scholar 

  59. P. Krčál, L. Mokrushin, P. Thiagarajan, W. Yi, Timed vs. time-triggered automata, in CONCUR 2004-Concurrency Theory (Springer, Berlin, 2004), pp. 340–354

    Google Scholar 

  60. M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, D. Teneketzis, Diagnosability of discrete-event systems. IEEE Trans. Autom. Control 40(9), 1555–1575 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  61. S. Tripakis, Fault diagnosis for timed automata, in International Symposium on Formal Techniques in Real-Time and Fault-Tolerant Systems (Springer, Berlin, 2002), pp. 205–221

    Book  MATH  Google Scholar 

  62. E. Schweitzer, B. Fleming, T.J. Lee, P.M. Anderson et al., Reliability analysis of transmission protection using fault tree methods, in Proceedings of the 24th Annual Western Protective Relay Conference (1997), pp. 1–17

    Google Scholar 

Download references

Acknowledgements

This work is funded in part by the National Science Foundation under the award number CNS-1329803. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NSF. The authors would like to thank Rishabh Jain, Srdjn Lukic, Saqib Hasan, Scott Eisele, and Amogh Kulkarni for their help and discussions related to the work presented here.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ajay Chhokra .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chhokra, A., Dubey, A., Mahadevan, N., Hasan, S., Karsai, G. (2018). Diagnosis in Cyber-Physical Systems with Fault Protection Assemblies. In: Sayed-Mouchaweh, M. (eds) Diagnosability, Security and Safety of Hybrid Dynamic and Cyber-Physical Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-74962-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-74962-4_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74961-7

  • Online ISBN: 978-3-319-74962-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics