Nothing Special   »   [go: up one dir, main page]

Skip to main content

Engine Parameter Outlier Detection: Verification by Simulating PID Controllers Generated by Genetic Algorithm

  • Conference paper
Advances in Intelligent Data Analysis XI (IDA 2012)

Abstract

We propose a method for engine configuration diagnostics based on clustering of engine parameters. The method is tested using simulation of PID controller parameters generated and selected using a genetic algorithm. The parameter analysis is based on a state-of-the art method using multivariate extreme value statistics for outlier detection. This method is modified using a variational mixture model which automatically defines a number of Gaussian kernels and replaces a Gaussian mixture model.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /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
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Agovic, A., Banerjee, A., Ganguly, A.R., Protopopescu, V.A.: Anomaly detection in transportation corridors using manifold embedding. In: Proceedings of the First International Workshop on Knowledge Discovery from Sensor Data, ACM KDD Conference, San Jose, CA (2007)

    Google Scholar 

  2. Alander, J.T.: Indexed Bibliography of Genetic Algorithms in Control, Report No. 94-1-CONTROL, University of Vaasa, Department of Information Technology and Production Economics, University of Vaasa (1995), http://lipas.uwasa.fi/~TAU/reports/report94-1/gaCONTROLbib.pdf

  3. Alander, J.T.: Indexed Bibliography of Genetic Algorithms in Machine Learning, Report No. 94-1-ML, Department of Electrical Engineering and Automation, University of Vaasa (2007), http://lipas.uwasa.fi/~TAU/reports/report94-1/gaMLbib.pdf

  4. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2007)

    Google Scholar 

  5. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A Survey. ACM Computing Surveys 41(3), 1–72 (2009)

    Article  Google Scholar 

  6. Clifton, D.A.: Condition monitoring of gas-turbine engines. Transfer Report, Department of Engineering Science, University of Oxford (2005)

    Google Scholar 

  7. Clifton, D.A., Hugueny, S., Tarassenko, L.: Novelty detection with multivariate extreme value statistics. Journal of Signal Processing Systems 65, 371–389 (2011)

    Article  Google Scholar 

  8. Haugen, F., Fjelddalen, E., Dunia, R., Edgar, T.F.: Demonstrating PID control principles using an Air Heater and LabVIEW. CACHE News (Computer Aids for Chemical Engineering) (Winter 2007)

    Google Scholar 

  9. Hugueny, S., Clifton, D.A., Tarassenko, L.: Probabilistic Patient Monitoring with Multivariate, Multimodal Extreme Value Theory. In: Fred, A., Filipe, J., Gamboa, H. (eds.) BIOSTEC 2010. CCIS, vol. 127, pp. 199–211. Springer, Heidelberg (2011) (Invited article, from IEEE Biomedical Engineering Systems and Technologies)

    Chapter  Google Scholar 

  10. Lewis, P.H., Yang, C.: Basic Control Systems Engineering. Prentice-Hall Inc. (1997)

    Google Scholar 

  11. Mantere, T., Alander, J.T.: Evolutionary Software Engineering, a Review. Applied Soft Computing 5(3), 315–331 (2005)

    Article  Google Scholar 

  12. Mohamed, F.A., Koivo, H.N.: Diesel engine systems with genetic algorithm self tuning PID controller. Technical Report, Control Engineering Lab, Helsinki University of Technology (2005)

    Google Scholar 

  13. Olsson, J.: Automatic tuning of control parameters for single speed engines. Master’s Thesis. Stockholm, The Royal Institute of Technology, November 22 (2004)

    Google Scholar 

  14. Pedersen, G.K.M.: Towards automatic controller design using multi-objective evolutionary algorithms. Ph.D. Thesis, Department of Control Engineering, Aalborg University (2005)

    Google Scholar 

  15. Rakopoulos, C.D., Giakoumis, E.G.: Availability analysis of a turbocharged diesel engine operating under transient load conditions. Energy 29, 1085–1104 (2004)

    Article  Google Scholar 

  16. Roberts, S.J.: Extreme value statistics for novelty detection in biomedical data processing. In: IEE Proceedings - Science, Measurement and Technology, vol. 147, pp. 363–367 (2000)

    Google Scholar 

  17. Sundaram, I.S., Strachan, I.G.D., Clifton, D.A., Tarassenko, L., King, S.: Aircraft engine health monitoring using density modeling and extreme value statistics. In: Proc. 6th Intern. Conference on Condition Monitoring and Machinery Failure Prevention Technologies, Dublin, Ireland, pp. 919–930 (2009)

    Google Scholar 

  18. Variational Bayesian Expectation Maximization for Gaussian Mixture Models, http://www.cs.ubc.ca/~murphyk/Software/VBEMGMM/index.html

  19. Törmänen, P.: Evaluating the benefit of fuzzy logic for PID-control by means of genetic algorithms - case: frequency controller. University of Vaasa. Master’s thesis 31-32 (1997)

    Google Scholar 

  20. Åström, K., Hägglund T. H.: PID controllers: Theory, Design and Tuning. Instrument Society of America (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Vesterback, J. et al. (2012). Engine Parameter Outlier Detection: Verification by Simulating PID Controllers Generated by Genetic Algorithm. In: Hollmén, J., Klawonn, F., Tucker, A. (eds) Advances in Intelligent Data Analysis XI. IDA 2012. Lecture Notes in Computer Science, vol 7619. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34156-4_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34156-4_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34155-7

  • Online ISBN: 978-3-642-34156-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics