Monitoring Equipment Operation Through Model and Event Discovery

  • Sławomir NowaczykEmail author
  • Anita Sant’Anna
  • Ece Calikus
  • Yuantao Fan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11315)


Monitoring the operation of complex systems in real-time is becoming both required and enabled by current IoT solutions. Predicting faults and optimising productivity requires autonomous methods that work without extensive human supervision. One way to automatically detect deviating operation is to identify groups of peers, or similar systems, and evaluate how well each individual conforms with the group.

We propose a monitoring approach that can construct knowledge more autonomously and relies on human experts to a lesser degree: without requiring the designer to think of all possible faults beforehand; able to do the best possible with signals that are already available, without the need for dedicated new sensors; scaling up to “one more system and component” and multiple variants; and finally, one that will adapt to changes over time and remain relevant throughout the lifetime of the system.


  1. 1.
    Alippi, C., Roveri, M., Trovò, F.: A “Learning from Models” cognitive fault diagnosis system. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012. LNCS, vol. 7553, pp. 305–313. Springer, Heidelberg (2012). Scholar
  2. 2.
    Alippi, C., Roveri, M., Trovò, F.: A self-building and cluster-based cognitive fault diagnosis system for sensor networks. IEEE Trans. Neural Netw. Learn. Syst. 25(6), 1021–1032 (2014)CrossRefGoogle Scholar
  3. 3.
    Byttner, S., Nowaczyk, S., Prytz, R., Rögnvaldsson, T.: A field test with self-organized modeling for knowledge discovery in a fleet of city buses. In: IEEE ICMA, pp. 896–901 (2013)Google Scholar
  4. 4.
    Chen, H., Tiňo, P., Rodan, A., Yao, X.: Learning in the model space for cognitive fault diagnosis. IEEE TNNLS 25(1), 124–136 (2014)Google Scholar
  5. 5.
    D’Silva, S.H.: Diagnostics based on the statistical correlation of sensors. Technical paper 2008-01-0129. Society of Automotive Engineers (SAE) (2008)Google Scholar
  6. 6.
    Fan, Y., Nowaczyk, S., Rögnvaldsson, T.: Evaluation of self-organized approach for predicting compressor faults in a city bus fleet. Procedia Comput. Sci. 53, 447–456 (2015)CrossRefGoogle Scholar
  7. 7.
    Fan, Y., Nowaczyk, S., Rögnvaldsson, T.: Incorporating expert knowledge into a self-organized approach for predicting compressor faults in a city bus fleet. Frontiers in Artificial Intelligence and Applications, vol. 278, pp. 58–67 (2015)Google Scholar
  8. 8.
    Filev, D.P., Chinnam, R.B., Tseng, F., Baruah, P.: An industrial strength novelty detection framework for autonomous equipment monitoring and diagnostics. IEEE Trans. Ind. Inform. 6, 767–779 (2010)CrossRefGoogle Scholar
  9. 9.
    Filev, D.P., Tseng, F.: Real time novelty detection modeling for machine health prognostics. In: North American Fuzzy Information Processing Society (2006)Google Scholar
  10. 10.
    Fogelstrom, K.A.: Air brake system characterization by self learning algorithm (2006)Google Scholar
  11. 11.
    Fogelstrom, K.A.: Prognostic and diagnostic system for air brakes (2007)Google Scholar
  12. 12.
    Gadd, H., Werner, S.: Fault detection in district heating substations. Appl. Energy 157, 51–59 (2015)CrossRefGoogle Scholar
  13. 13.
    Hansson, J., Svensson, M., Rögnvaldsson, T., Byttner, S.: Remote diagnosis modelling (2013)Google Scholar
  14. 14.
    Kargupta, H., et al.: VEDAS: a mobile and distributed data stream mining system for real-time vehicle monitoring. In: Fourth International Conference on Data Mining (2004)Google Scholar
  15. 15.
    Kargupta, H., et al.: MineFleet: the vehicle data stream mining system for ubiquitous environments. In: May, M., Saitta, L. (eds.) Ubiquitous Knowledge Discovery. LNCS (LNAI), vol. 6202, pp. 235–254. Springer, Heidelberg (2010). Scholar
  16. 16.
    Kargupta, H., Puttagunta, V., Klein, M., Sarkar, K.: On-board vehicle data stream monitoring using mine-fleet and fast resource constrained monitoring of correlation matrices. New Gener. Comput. 25, 5–32 (2007)CrossRefGoogle Scholar
  17. 17.
    Lapira, E.R.: Fault detection in a network of similar machines using clustering approach. Ph.D. thesis, University of Cincinnati (2012)Google Scholar
  18. 18.
    Lapira, E.R., Al-Atat, H., Lee, J.: Turbine-to-turbine prognostics technique for wind farms (2011)Google Scholar
  19. 19.
    Quevedo, J., et al.: Combining learning in model space fault diagnosis with data validation/reconstruction: application to the Barcelona water network. Eng. Appl. Artif. Intell. 30, 18–29 (2014)CrossRefGoogle Scholar
  20. 20.
    Rögnvaldsson, T., Nowaczyk, S., Byttner, S., Prytz, R., Svensson, M.: Self-monitoring for maintenance of vehicle fleets. Data Min. Knowl. Discov. 32(2), 344–384 (2018)CrossRefGoogle Scholar
  21. 21.
    Theissler, A.: Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection. Knowl.-Based Syst. 123, 163–173 (2017)CrossRefGoogle Scholar
  22. 22.
    Vachkov, G.: Intelligent data analysis for performance evaluation and fault diagnosis in complex systems. In: IEEE ICFS, pp. 6322–6329 (2006)Google Scholar
  23. 23.
    Zhang, Y., Gantt Jr., G.W., Rychlinski, M.J., Edwards, R.M., Correia, J.J., Wolf, C.E.: Connected vehicle diagnostics and prognostics, concept, and initial practice. IEEE Trans. Reliab. 58, 286–294 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sławomir Nowaczyk
    • 1
    Email author
  • Anita Sant’Anna
    • 1
  • Ece Calikus
    • 1
  • Yuantao Fan
    • 1
  1. 1.CAISR, Halmstad UniversityHalmstadSweden

Personalised recommendations