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

  • Joni Vesterback
  • Vladimir Bochko
  • Mika Ruohonen
  • Jarmo Alander
  • Andreas Bäck
  • Martin Nylund
  • Allan Dal
  • Fredrik Östman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)

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.

Keywords

multivariate outlier analysis PID controller genetic algorithm simulation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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. 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. 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. 4.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2007)Google Scholar
  5. 5.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A Survey. ACM Computing Surveys 41(3), 1–72 (2009)CrossRefGoogle Scholar
  6. 6.
    Clifton, D.A.: Condition monitoring of gas-turbine engines. Transfer Report, Department of Engineering Science, University of Oxford (2005)Google Scholar
  7. 7.
    Clifton, D.A., Hugueny, S., Tarassenko, L.: Novelty detection with multivariate extreme value statistics. Journal of Signal Processing Systems 65, 371–389 (2011)CrossRefGoogle Scholar
  8. 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. 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)CrossRefGoogle Scholar
  10. 10.
    Lewis, P.H., Yang, C.: Basic Control Systems Engineering. Prentice-Hall Inc. (1997)Google Scholar
  11. 11.
    Mantere, T., Alander, J.T.: Evolutionary Software Engineering, a Review. Applied Soft Computing 5(3), 315–331 (2005)CrossRefGoogle Scholar
  12. 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. 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. 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. 15.
    Rakopoulos, C.D., Giakoumis, E.G.: Availability analysis of a turbocharged diesel engine operating under transient load conditions. Energy 29, 1085–1104 (2004)CrossRefGoogle Scholar
  16. 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. 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. 18.
    Variational Bayesian Expectation Maximization for Gaussian Mixture Models, http://www.cs.ubc.ca/~murphyk/Software/VBEMGMM/index.html
  19. 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. 20.
    Åström, K., Hägglund T. H.: PID controllers: Theory, Design and Tuning. Instrument Society of America (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Joni Vesterback
    • 1
  • Vladimir Bochko
    • 1
  • Mika Ruohonen
    • 1
  • Jarmo Alander
    • 1
  • Andreas Bäck
    • 2
  • Martin Nylund
    • 2
  • Allan Dal
    • 2
  • Fredrik Östman
    • 2
  1. 1.Department of Electrical Engineering and Energy TechnologyUniversity of VaasaVaasaFinland
  2. 2.Wärtsilä Finland OyVaasaFinland

Personalised recommendations