Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

Monitoring Equipment Operation Through Model and Event Discovery

  • Conference paper
  • First Online:

Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 11315))

  • 1191Accesses

Abstract

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.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • 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

References

  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).https://doi.org/10.1007/978-3-642-33266-1_38

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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. Chen, H., Tiňo, P., Rodan, A., Yao, X.: Learning in the model space for cognitive fault diagnosis. IEEE TNNLS25(1), 124–136 (2014)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. Fogelstrom, K.A.: Air brake system characterization by self learning algorithm (2006)

    Google Scholar 

  11. Fogelstrom, K.A.: Prognostic and diagnostic system for air brakes (2007)

    Google Scholar 

  12. Gadd, H., Werner, S.: Fault detection in district heating substations. Appl. Energy157, 51–59 (2015)

    Article  Google Scholar 

  13. Hansson, J., Svensson, M., Rögnvaldsson, T., Byttner, S.: Remote diagnosis modelling (2013)

    Google Scholar 

  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. 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).https://doi.org/10.1007/978-3-642-16392-0_14

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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. Lapira, E.R., Al-Atat, H., Lee, J.: Turbine-to-turbine prognostics technique for wind farms (2011)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  21. Theissler, A.: Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection. Knowl.-Based Syst.123, 163–173 (2017)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CAISR, Halmstad University, Halmstad, Sweden

    Sławomir Nowaczyk, Anita Sant’Anna, Ece Calikus & Yuantao Fan

Authors
  1. Sławomir Nowaczyk

    You can also search for this author inPubMed Google Scholar

  2. Anita Sant’Anna

    You can also search for this author inPubMed Google Scholar

  3. Ece Calikus

    You can also search for this author inPubMed Google Scholar

  4. Yuantao Fan

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toSławomir Nowaczyk.

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Rm 209, Building B, Autonomous University of Madrid, Madrid, Spain

    David Camacho

  3. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

  4. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

Rights and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nowaczyk, S., Sant’Anna, A., Calikus, E., Fan, Y. (2018). Monitoring Equipment Operation Through Model and Event Discovery. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11315. Springer, Cham. https://doi.org/10.1007/978-3-030-03496-2_6

Download citation

Publish with us

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • 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

[8]ページ先頭
©2009-2025 Movatter.jp