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Drift Detection and Characterization for Fault Diagnosis and Prognosis of Dynamical Systems

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7520)

Abstract

In this paper, we present a methodology for drift detection and characterization. Our methodology is based on extracting indicators that reflect the health state of a system. It is situated in an architecture of fault diagnosis/prognosis of dynamical system that we present in this paper. A dynamical clustering algorithm is used as a major tool. The feature vectors are clustered and then the parameters of these clusters are updated as each feature vector arrives. The cluster parameters serve to compute indicators for drift detection and characterization. Then, a prognosis block uses these drift indicators to estimate the remaining useful life. The architecture is tested on a case study of a tank system with different scenarios of single and multiple faults, and with different dynamics of drift.

Keywords

  • Fault Diagnosis
  • Prognosis
  • Drift
  • Dynamical Clustering

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References

  1. Basseville, M., Nikiforov, I.: Detection of Abrupt Changes: Theory and Application. Prentice Hall, Inc. (1993)

    Google Scholar 

  2. Boubacar, H.A., Lecoueuche, S., Maouche, S.: Audyc neural network using a new gaussian densities merge mechanism. In: 7th Conference on Adaptive and Neural Computing Algorithms, pp. 155–158 (2004)

    Google Scholar 

  3. Brotherton, T., Jahns, G., Jacobs, J., Wroblewski, D.: Prognosis of faults in gas turbine engines. In: IEEE Aerospace Conference (2000)

    Google Scholar 

  4. Byington, C., Roemer, M., Kacprzynski, G., Galie, T.: Prognostic enhancements to diagnostic systems for improved condition-based maintenance. In: IEEE Aerospace Conference (2002)

    Google Scholar 

  5. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. Technical report, ACM Computing Surveys (2009)

    Google Scholar 

  6. Isermann, R.: Model-based fault detection and diagnosis - status and applications. Annual Reviews in Control 29, 71–85 (2005)

    CrossRef  Google Scholar 

  7. Jardine, A.K.S., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 20, 1483–1510 (2006)

    CrossRef  Google Scholar 

  8. Kuncheva, L.: Using control charts for detecting concept change in streaming data. Technical report, Technical Report BCS-TR-001 (2009)

    Google Scholar 

  9. Kuncheva, L.I.: Classifier Ensembles for Changing Environments. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 1–15. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

  10. Lecoueuche, S., Lurette, C.: Auto-Adaptive and Dynamical Clustering Neural Network. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 350–358. Springer, Heidelberg (2003)

    CrossRef  Google Scholar 

  11. Li, G., Qin, J., Ji, Y., Zhou, D.-H.: Reconstruction based fault prognosis for continuous processes. Control Engineering Practice 18, 1211–1219 (2010)

    CrossRef  Google Scholar 

  12. Iverson, D.L.: Inductive system health monitoring. In: Proceedings of The 2004 International Conference on Artificial Intelligence (IC-AI 2004). CSREA Press, Las Vegas (2004)

    Google Scholar 

  13. Markou, M., Singh, S.: Novelty detection: a review–part i: statistical approaches. Signal Processing 83, 2481–2497 (2003)

    CrossRef  MATH  Google Scholar 

  14. Meeker, W.Q., Escobar, L.A.: Statistical Methods for Reliability Data. Wiley series in probability and statistics, applied probability and statistics section. Jhon Wiley and Sons, New York (1998)

    MATH  Google Scholar 

  15. Minku, L.L., Yao, X.: Ddd: A new ensemble approach for dealing with concept drift. IEEE Transactions on Knowledge and Data Engineering (2011)

    Google Scholar 

  16. Muller, A., Marquez, A.C., Iunga, B.: On the concept of e-maintenance: Review and current research. Reliability Engineering and System Safety 93, 1165–1187 (2008)

    CrossRef  Google Scholar 

  17. MvBain, J., Timusk, M.: Fault detection in variable speed machinery: Statistical parametrization. Journal of Sound and Vibration 327, 623–646 (2009)

    CrossRef  Google Scholar 

  18. Noortwijk, V.: A survey of the application of gamma process in maintenance. Reliability Engineering & System Safety 94, 2–21 (2009)

    CrossRef  Google Scholar 

  19. Oppenheimer, C., Loparo, K.: Physically based diagnosis and prognosis of cracked rotor shafts. In: Preceeding of SPIE (2002)

    Google Scholar 

  20. Peysson, F., Boubezoul, A., Oulasdine, M., Outbib, R.: A data driven prognostic methodology without a priori knowledge. In: Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (2009)

    Google Scholar 

  21. Peysson, F., Oulasdine, M., Outbib, R., Leger, J.-B., Myx, O., Allemand, C.: Damage trajectory analysis based prognostic. In: International Conference on Prognostics and Health Management, PHM 2008 (2008)

    Google Scholar 

  22. Sayed-Mouchaweh, M.: Semi-supervised classification method for dynamic applications. Fuzzy Sets and Systems 161, 544–563 (2012)

    CrossRef  MathSciNet  Google Scholar 

  23. Si, X.-S., Wang, W., Hu, C.-H., Zhou, D.-H.: Remaining useful life estimation - a review on the statistical data driven approaches. European Journal of Operational Research 213, 1–14 (2011)

    CrossRef  MathSciNet  Google Scholar 

  24. Traore, M., Duviella, E., Lecoeuche, S.: Comparison of two prognosis methods based on neuro fuzzy inference system and clustering neural network. In: SAFE PROCESS (2009)

    Google Scholar 

  25. Tsymbal, A.: The problem of concept drift: definitions and related work. Trinity College, Dublin, Ireland, TCD-CS-2004-15 (2004)

    Google Scholar 

  26. Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., Wu, B.: Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Jhon Wiley and Sons, Inc. (2006)

    Google Scholar 

  27. Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.: A review of process fault detection and diagnosis part iii: Process history based methods. Computers and Chemical Engineering 27, 327–346 (2003)

    CrossRef  Google Scholar 

  28. Yan, J., Lee, J., Koc, M.: Predictive algorithm for machine degradation using logistic regression. In: MIM 2002 (2002)

    Google Scholar 

  29. Indre zliobate. Learning under concept drift: an overview. Technical report, Vilnius University (2009)

    Google Scholar 

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Chammas, A., Sayed-Mouchaweh, M., Duviella, E., Lecoeuche, S. (2012). Drift Detection and Characterization for Fault Diagnosis and Prognosis of Dynamical Systems. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds) Scalable Uncertainty Management. SUM 2012. Lecture Notes in Computer Science(), vol 7520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33362-0_9

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  • DOI: https://doi.org/10.1007/978-3-642-33362-0_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33361-3

  • Online ISBN: 978-3-642-33362-0

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