Applied Intelligence

, Volume 37, Issue 1, pp 60–79 | Cite as

Monitoring of complex systems of interacting dynamic systems

  • Michael E. Cholette
  • Jianbo Liu
  • Dragan Djurdjanovic
  • Kenneth A. Marko
Article

Abstract

Increases in functionality, power and intelligence of modern engineered systems led to complex systems with a large number of interconnected dynamic subsystems. In such machines, faults in one subsystem can cascade and affect the behavior of numerous other subsystems. This complicates the traditional fault monitoring procedures because of the need to train models of the faults that the monitoring system needs to detect and recognize. Unavoidable design defects, quality variations and different usage patterns make it infeasible to foresee all possible faults, resulting in limited diagnostic coverage that can only deal with previously anticipated and modeled failures. This leads to missed detections and costly blind swapping of acceptable components because of one’s inability to accurately isolate the source of previously unseen anomalies. To circumvent these difficulties, a new paradigm for diagnostic systems is proposed and discussed in this paper. Its feasibility is demonstrated through application examples in automotive engine diagnostics.

Keywords

Fault detection and diagnosis Distributed anomaly detection Automotive engine diagnostics 

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References

  1. 1.
    Gómez M, Ventosa J (1998) Expert system hardware for fault detection. Appl Intell Google Scholar
  2. 2.
    Nott C, Ölçmen SM, Karr CL, Trevino LC (2006) SR-30 Turbojet engine real-time sensor health monitoring using neural networks, and Bayesian belief networks. Appl Intell 26:251–265 CrossRefGoogle Scholar
  3. 3.
    Feret MP, Glasgow JI (1997) Combining case-based and model-based reasoning for the diagnosis of complex devices. Appl Intell 7:57–78 CrossRefGoogle Scholar
  4. 4.
    Bahrampour S, Moshiri B, Salahshoor K (2010) Weighted and constrained possibilistic C-means clustering for online fault detection and isolation. Appl Intell 1–16 Google Scholar
  5. 5.
    Liu J, Djurdjanovic D, Marko KA, Ni J (2009) A novel method for anomaly detection, localization and fault isolation for dynamic control systems. Mech Syst Signal Process 23(8):2488–2499 CrossRefGoogle Scholar
  6. 6.
    Liu J, Djurdjanovic D, Marko K, Ni J (2009) Growing structure multiple model system for anomaly detection and fault diagnosis. J Dyn Syst Meas Control 131(5):051001 CrossRefGoogle Scholar
  7. 7.
    Liu J, Sun P, Djurdjanovic D, Marko KA, Ni J (2006) Growing structure multiple model system based anomaly detection for crankshaft monitoring. In: Proc of the 2006 international symposium on neural networks (ISNN), Chengdu, China, pp 396–405 Google Scholar
  8. 8.
    Cholette M, Djurdjanovic D (2011) Precedent-free fault isolation in a diesel engine EGR system. J Dyn Syst Meas Control (in review) Google Scholar
  9. 9.
    Djurdjanovic D, Hearn C, Liu Y (2010) Immune systems inspired approach to anomaly detection, fault localization and diagnosis in a generator. In: Proc of the 2010 conf on grand challenges in modeling and simulation (GCMS), Ottawa, ON, July 11–14, Paper no 71 Google Scholar
  10. 10.
    Cohen L (1995) Time-frequency analysis. Prentice Hall, Englewood Cliffs Google Scholar
  11. 11.
    Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York MATHGoogle Scholar
  12. 12.
    Lee J, Ni J, Djurdjanovic D, Qiu H, Liao H (2006) Intelligent prognostics tools and e-maintenance. Comput Ind 57(6):476–489 CrossRefGoogle Scholar
  13. 13.
    Djurdjanovic D, Kegg R, Lee J, Ni J (2010) Generic multisensor based assessment of performance of manufacturing processes. Trans NAMRI/SME 38:379–386 Google Scholar
  14. 14.
    Kohonen T (1995) Self organizing maps. Springer series in information sciences. Springer, Berlin Google Scholar
  15. 15.
    Principe JC, Wang L, Motter MA (1998) Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control. Proc IEEE 86(11):2240–2258 CrossRefGoogle Scholar
  16. 16.
    Johansen TA, Foss BA (1995) Identification of non-linear system structure and parameters using regime decomposition. Automatica 31(2):321–326 MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Barreto GA, Araujo AFR (2004) Identification and control of dynamical systems using the self-organizing map. IEEE Trans Neural Netw 15(5):1244–1259 CrossRefGoogle Scholar
  18. 18.
    Fritzke B (1995) A growing neural gas network learns topologies. In: Advances in neural information processing systems, vol 7. MIT Press, Cambridge, pp 625–632 Google Scholar
  19. 19.
    Fritzke B (1994) Growing cell structures—a self-organizing network for unsupervised and supervised learning. Neural Netw 7(9):1441–1460 CrossRefGoogle Scholar
  20. 20.
    Alahakoon D, Halgamuge SK, Srinivasan B (2000) Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Trans Neural Netw 11(3):601–614 CrossRefGoogle Scholar
  21. 21.
    Liu J, Djurdjanovic D (2008) Topology preservation and cooperative learning in identification of multiple model systems. IEEE Trans Neural Netw 19(12):2065–2072 CrossRefGoogle Scholar
  22. 22.
    McLachlan G, Peel D (2000) Finite mixture models. Wiley, New York CrossRefMATHGoogle Scholar
  23. 23.
    Zivkovic Z, van der Heijden F (2004) Recursive unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell 26(5):651–656 CrossRefGoogle Scholar
  24. 24.
    ETAS GmbH (2007) Gasoline Engine Vehicle Model V5.0, Stuttgart, Germany Google Scholar
  25. 25.
    Montgomery DC (2001) Introduction to statistical quality control, 4th edn. Wiley, New York Google Scholar
  26. 26.
    TESIS DYNAware. en-DYNA® THERMOS® 2.0 Block Reference Manual, June 2006 Google Scholar
  27. 27.
    Latronico E, Ni J, Jiang L (2008) A novel method for input selection for the modeling of nonlinear dynamic systems. In: Proc of the ASME dynamic systems and control conference, October 2008 Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Michael E. Cholette
    • 1
  • Jianbo Liu
    • 2
  • Dragan Djurdjanovic
    • 1
  • Kenneth A. Marko
    • 3
  1. 1.University of Texas at AustinAustinUSA
  2. 2.Michigan State UniversityLansingUSA
  3. 3.University of MichiganAnn ArborUSA

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