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Clonal optimization-based negative selection algorithm with applications in motor fault detection

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Abstract

The Negative Selection Algorithm (NSA) and clonal selection method are two typical kinds of artificial immune systems. In this paper, we first introduce their underlying inspirations and working principles. It is well known that the regular NSA detectors are not guaranteed to always occupy the maximal coverage of the nonself space. Therefore, we next employ the clonal optimization method to optimize these detectors so that the best anomaly detection performance can be achieved. A new motor fault detection scheme using the proposed NSA is also presented and discussed. We demonstrate the efficiency of our approach with an interesting example of motor bearings fault detection, in which the detection rates of three bearings faults are significantly improved.

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References

  1. Goldsby GA, Kindt TJ, Kuby J, Osborne BA (2003) Immunology, 5th edn. W. H. Freeman and Company, New York

    Google Scholar 

  2. de Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence approach. Springer, London

    MATH  Google Scholar 

  3. Dasgupta D, Attoh-Okine N (1997) Immunity-based systems: a survey. In: Proceedings of IEEE international conference on systems, man, and cybernetics. Orlando, pp 369–374

  4. Dasgupta D, Nino F (2009) Immunological computation: theory and applications. Auerbach Publications, Boca Rato

    Google Scholar 

  5. Forrest S, Perelson AS, Allen L, Cherukuri R (1994) Self-nonself discrimination in a computer. In: Proceedings of IEEE symposium on research in security and privacy. Los Alamos, pp 202–212

  6. Dasgupta D, González F (2002) An immunity-based technique to characterize intrusions in computer networks. IEEE Trans Evol Comput 6(3):281–291. doi:10.1109/TEVC.2002.1011541

    Article  Google Scholar 

  7. Dasgupta D, Forrest S (1995) Tool breakage detection in milling operations using a negative selection algorithm. University of New Mexico, Albuquerque

    Google Scholar 

  8. González F (2003) A study of artificial immune systems applied to anomaly detection. University of Memphis, Memphis

    Google Scholar 

  9. Ayara M, Timmis J, de Lemos R, de Castro LN, Duncan R (2002) Negative selection: how to generate detectors. In: Proceedings of the 1st international conference on artificial immune systems. Canterbury, pp 89–98

  10. González F, Dasgupta D, Nino LF (2003) A randomized real-value negative selection algorithm. In: Proceedings of the 2nd international conference on artificial immune systems. Edinburgh, pp 261–272

  11. Gao XZ, Ovaska SJ, Wang X, Chow MY (2008) A neural networks-based negative selection algorithm in fault diagnosis. Neural Comput Appl 17(1):91–98. doi:10.1007/s00521-007-0092-z

    Google Scholar 

  12. Gao XZ, Ovaska SJ, Wang X, Chow MY (2007) A hierarchical optimization scheme for negative selection algorithm detectors in motor fault detection. In: Proceedings of the IEEE three-rivers workshop on soft computing in industrial applications. Passau, pp 163–169

  13. Gao XZ, Ovaska SJ, Wang X (2007) Particle swarm optimization of detectors in negative selection algorithm. In: Proceedings of the IEEE international conference on systems, man, and cybernetics. Montreal, pp 1236–1242

  14. Gao XZ, Ovaska SJ, Wang X (2008) A GA-based negative selection algorithm. Int J Innov Comput Inf Contr 4(4):971–979

    Google Scholar 

  15. Gao XZ, Ovaska SJ, Wang X (2008) Re-editing and censoring of detectors in negative selection algorithm. In: Proceedings of the 2nd international and interdisciplinary conference on adaptive knowledge representation and reasoning. Porvoo, pp 23–30

  16. Wang X, Gao XZ, Ovaska SJ (2004) Artificial immune optimization methods and applications—a survey. In: Proceedings of the IEEE international conference on systems, man, and cybernetics. Hague, pp 3415–3420

  17. de Castro LN, von Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6(3):239–251. doi:10.1109/TEVC.2002.1011539

    Article  Google Scholar 

  18. Ada GL, Nossal GJV (1987) The clonal selection theory. Sci Am 257(2):50–57

    Article  Google Scholar 

  19. Poli R, Langdon WB (2002) Foundations of genetic programming. Springer, Berlin

    MATH  Google Scholar 

  20. Yoo J, Hajela P (1999) Immune network simulations in multicriterion design. Struct Optim 18(2–3):85–94. doi:10.1007/BF01195983

    Google Scholar 

  21. Wang X, Gao XZ, Ovaska SJ (2007) A hybrid optimization algorithm based on ant colony and immune principles. Int J Comput Sci Appl 4(3):30–44

    Google Scholar 

  22. Frank PM (1990) Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—a survey and some new results. Automatica 26(3):459–474. doi:10.1016/0005-1098(90)90018-D

    Article  MATH  Google Scholar 

  23. Chow MY (1997) Methodologies of using neural network and fuzzy logic technologies for motor incipient fault detection. World Scientific Publishing Co. Pte. Ltd, Singapore

    Google Scholar 

  24. Li B, Chow MY, Tipsuwan Y, Hung JC (2000) Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans Ind Electron 47(5):1060–1069. doi:10.1109/41.873214

    Article  Google Scholar 

Download references

Acknowledgments

This research work was funded by the Academy of Finland under Grants 214144 and 124721. The authors would like to thank the anonymous reviewers for their insightful comments and constructive suggestions that have improved the paper.

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Authors and Affiliations

  1. Department of Electrical Engineering, Helsinki University of Technology, Otakaari 5 A, 02150, Espoo, Finland

    X. Z. Gao, S. J. Ovaska & X. Wang

  2. Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, 27695, USA

    M.-Y. Chow

Authors
  1. X. Z. Gao
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  2. S. J. Ovaska
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  3. X. Wang
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  4. M.-Y. Chow
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Corresponding author

Correspondence to X. Z. Gao.

Appendix

Appendix

Detectors generated in the regular NSA:

$$ \left[ {\begin{array}{*{20}c} { - 0. 0 7 8 9} & { 0. 0 7 6 6} & { - 0. 0 9 1 9} & { 0. 0 9 8 4} & { 0. 0 7 6 2} & { 0. 0 6 9 2} & { 0. 0 7 8 4} & { - 0. 0 8 5 2} & { 0. 0 9 6 3} & { 0. 0 9 5 3} \\ { 0. 0 9 4 5} & { 0. 0 8 8 7} & { 0. 0 9 6 5} & { - 0. 0 9 8 9} & { - 0. 0 8 1 3} & { 0. 0 6 7 9} & { 0. 0 8 1 7} & { - 0. 0 2 6 4} & { - 0. 0 9 0 2} & { 0. 0 9 6 1} \\ { - 0. 0 6 5 4} & { 0. 0 9 7 6} & { - 0. 0 7 4 6} & { 0. 0 8 1 6} & { 0. 0 9 9 8} & { 0. 0 9 6 0} & { 0. 0 3 4 2} & { - 0. 0 9 0 1} & { 0. 0 9 0 6} & { 0. 0 7 3 2} \\ { 0. 0 8 7 1} & { 0. 0 8 7 8} & { - 0. 0 9 6 0} & { 0. 0 6 5 3} & { 0. 0 9 1 9} & { - 0. 0 7 7 4} & { 0. 0 8 0 4} & { 0. 0 8 9 6} & { 0. 0 9 4 0} & { 0. 0 5 8 5} \\ { 0. 0 7 9 5} & { 0. 0 9 8 7} & { 0. 0 9 1 1} & { 0 . 0 8 8 7} & { 0. 0 6 6 7} & { 0. 0 6 2 4} & { 0. 0 9 9 8} & { - 0. 0 2 8 2} & { 0. 0 7 8 0} & { 0. 0 6 4 9} \\ { 0. 0 9 9 7} & { 0. 0 4 8 3} & { 0. 0 9 1 8} & { 0. 0 7 0 7} & { 0. 0 9 8 0} & { - 0. 0 9 3 9} & { 0. 0 5 7 2} & { 0. 0 6 7 9} & { 0. 0 6 1 2} & { 0. 0 9 8 4} \\ { 0. 0 9 5 6} & { 0. 0 9 7 6} & { - 0. 0 5 8 9} & { 0. 0 5 1 6} & { 0. 0 7 1 8} & { 0. 0 9 5 9} & { 0. 0 9 2 0} & { 0. 0 6 1 9} & { - 0. 0 5 2 2} & { 0. 0 9 4 3} \\ { - 0. 0 8 7 3} & { 0. 0 7 1 5} & { - 0. 0 7 5 8} & { - 0. 0 9 4 8} & { 0. 0 9 2 5} & { - 0. 0 9 2 6} & { 0. 0 9 9 3} & { 0. 0 6 5 3} & { 0. 0 7 1 9} & { 0. 0 9 6 8} \\ { - 0. 0 2 5 0} & { 0. 0 9 2 0} & { 0. 0 8 9 8} & { 0. 0 9 9 2} & { - 0. 0 1 6 5} & { 0. 0 6 1 9} & { 0. 0 7 8 8} & { - 0. 0 9 9 1} & { 0. 0 8 4 3} & { 0. 0 9 8 5} \\ { 0. 0 5 0 6} & { 0. 0 5 8 5} & { 0. 0 2 4 9} & { 0. 0 9 9 6} & { 0 . 0 9 9 9} & { 0. 0 9 5 9} & { 0. 0 9 9 0} & { - 0. 0 0 9 7} & { 0. 0 9 0 0} & { 0. 0 9 7 3} \\ \end{array} } \right] $$

Clonal optimized detectors for single bearings fault detection:

$$ \begin{gathered} \left[ {\begin{array}{*{20}c} { - 0. 0 6 5 1} & { 0. 0 9 7 9} & { - 0. 0 7 4 7 } & { 0. 0 8 0 7} & { 0. 0 9 8 1} & { 0. 0 9 5 5} & { 0. 0 3 4 1} & { - 0. 0 9 0 9} & { 0. 0 8 9 7} & { 0 . 0 7 2 3} \\ { 0. 0 9 5 9} & { 0. 0 8 8 3} & { - 0. 1 0 0 5} & { 0. 0 9 3 6} & { 0. 0 7 3 0} & { - 0. 0 8 1 1} & { 0. 1 0 0 9} & { - 0. 0 8 0 6} & { 0. 0 3 6 8} & { - 0. 0 7 3 7} \\ { - 0. 0 7 0 2} & { 0. 0 9 5 1} & { - 0. 0 9 4 1} & { 0. 0 9 1 4} & { - 0. 0 6 5 1} & { 0. 0 8 9 5} & { 0. 0 7 7 4} & { - 0. 0 9 6 1} & { 0. 0 8 6 7} & { 0. 0 5 5 7} \\ { 0. 0 9 6 6} & { - 0. 0 9 7 7} & { 0. 0 8 0 9} & { 0. 0 9 8 0} & { - 0. 0 3 7 4} & { - 0. 0 2 8 8} & { 0. 0 9 7 1} & { 0. 0 8 0 6} & { - 0. 0 8 3 7} & { 0. 0 9 6 3} \\ { 0. 0 8 0 1} & { - 0. 0 6 3 8} & { - 0. 0 9 2 1} & { 0. 0 9 0 5} & { - 0. 0 8 6 0} & { 0 . 0 8 7 4} & { - 0. 0 7 9 3} & { 0. 0 9 7 0} & { 0. 0 5 8 3} & { 0. 0 8 6 1} \\ { 0. 0 8 1 3} & { 0. 0 9 7 4} & { 0. 0 9 7 8} & { - 0. 0 9 0 6} & { 0. 0 9 7 3} & { 0. 0 0 4 0} & { - 0. 0 9 9 6} & { - 0. 0 6 3 4} & { 0. 0 8 9 8} & { 0. 0 6 8 5} \\ { 0. 0 9 0 2} & { - 0. 0 8 9 6} & { 0. 0 6 4 9} & { 0. 0 8 5 6} & { - 0. 0 9 3 9} & { 0. 0 9 6 5} & { - 0. 0 9 9 0} & { 0. 0 1 7 3} & { - 0. 0 9 8 5} & { 0. 0 8 5 7} \\ { - 0. 0 9 1 4} & { 0. 1 0 0 2} & { - 0. 0 4 3 8} & { - 0. 0 2 1 4} & { - 0. 0 8 3 8} & { 0. 0 9 1 5} & { 0. 0 9 0 9} & { - 0. 0 8 9 5} & { 0. 0 9 5 1} & { 0. 0 9 8 9} \\ { 0. 0 9 3 3} & { 0. 0 7 5 1} & { - 0. 0 9 4 8} & { 0. 0 9 5 3} & { 0. 0 7 2 1} & { - 0. 0 8 9 5} & { 0. 0 9 3 1} & { - 0. 0 4 1 3} & { - 0. 0 9 0 0} & { 0. 0 8 3 6} \\ { 0. 0 5 4 5} & { 0. 0 9 3 6} & { - 0. 0 7 8 7} & { 0. 0 7 8 3} & { 0. 0 5 9 8} & { 0. 0 8 3 9} & { 0. 0 9 3 8} & { - 0. 0 9 3 4} & { 0. 0 7 8 5} & { - 0. 0 9 1 2} \\ \end{array} } \right] \hfill \\ \hfill \\ \end{gathered} $$

Clonal optimized detectors for dual bearings fault detection:

$$ \left[ {\begin{array}{*{20}c} { - 0. 0 6 5 4} & { 0. 0 9 7 3} & { - 0. 0 7 4 6} & { 0. 0 8 1 6} & { 0. 0 9 9 8} & { 0. 0 9 6 1} & { 0. 0 3 4 3} & { - 0. 0 9 0 2} & { 0. 0 9 0 5} & { 0. 0 7 2 9} \\ { 0. 0 9 4 5} & { 0. 0 8 8 4} & { - 0. 0 9 8 4} & { 0. 0 9 2 3} & { 0. 0 7 4 4} & { - 0. 0 8 1 5} & { 0. 0 9 9 9} & { - 0. 0 8 2 1} & { 0. 0 3 6 5} & { - 0. 0 7 2 1} \\ { - 0. 0 7 0 2} & { 0. 0 9 5 1} & { - 0. 0 9 4 2} & { 0. 0 9 2 1} & { - 0. 0 6 3 6} & { 0. 0 8 9 2} & { 0. 0 7 6 8} & { - 0. 0 9 5 6} & { 0. 0 8 7 2} & { 0. 0 5 6 7} \\ { 0. 0 9 6 7} & { - 0. 0 9 6 7} & { 0. 0 8 1 5} & { 0. 0 9 6 8} & { - 0. 0 3 5 9} & { - 0. 0 2 9 5} & { 0. 0 9 8 6} & { 0. 0 8 2 0} & { - 0 . 0 8 4 6} & { 0. 0 9 5 4} \\ { 0. 0 7 9 5} & { - 0. 0 6 5 4} & { - 0. 0 9 3 0} & { 0. 0 9 3 3} & { - 0. 0 8 5 5} & { 0. 0 8 6 4} & { - 0 . 0 8 0 3} & { 0. 0 9 9 1} & { 0. 0 5 8 4} & { 0. 0 8 8 2} \\ { 0. 0 7 9 5} & { 0. 0 9 9 4} & { 0. 0 9 9 2} & { - 0. 0 8 9 6} & { 0. 0 9 8 1} & { 0. 0 0 3 7} & { - 0 . 0 9 8 4} & { - 0. 0 6 3 2} & { 0. 0 8 9 5} & { 0. 0 6 9 0} \\ { 0. 0 9 2 2} & { - 0. 0 8 6 1} & { 0. 0 6 2 6} & { 0. 0 8 4 5} & { - 0. 0 9 7 9} & { 0. 0 9 6 9} & { - 0 . 0 9 6 9} & { 0. 0 1 5 1} & { - 0 . 0 9 8 8} & { 0. 0 8 4 5} \\ { - 0. 0 9 2 0} & { 0. 0 9 9 8} & { - 0. 0 4 2 9} & { - 0. 0 2 1 4} & { - 0. 0 8 4 4} & { 0. 0 9 0 1} & { 0. 0 9 2 4} & { - 0. 0 8 8 3} & { 0. 0 9 4 6} & { 0. 0 9 9 6} \\ { 0. 0 9 1 4} & { 0. 0 7 6 6} & { - 0. 0 9 5 4} & { 0. 0 9 6 7} & { 0. 0 7 5 6} & { - 0. 0 9 0 6} & { 0. 0 9 4 2} & { - 0. 0 4 2 2} & { - 0 . 0 9 0 2} & { 0. 0 8 4 2} \\ { 0. 0 5 9 0} & { 0. 0 9 6 8} & { - 0. 0 7 6 1} & { 0. 0 8 1 2} & { 0. 0 6 1 9} & { 0. 0 8 6 5} & { 0. 0 9 6 6} & { - 0 . 0 9 4 9} & { 0. 0 8 1 9} & { - 0. 0 9 5 2} \\ \end{array} } \right] $$

Clonal optimized detectors for triple bearings fault detection:

$$ \left[ {\begin{array}{*{20}c} { - 0. 0 6 5 2} & { 0. 0 9 7 3} & { - 0. 0 7 4 7} & { 0. 0 8 1 5} & { 0. 0 9 9 8} & { 0. 0 9 5 9} & { 0. 0 3 4 2} & { - 0. 0 9 0 1} & { 0. 0 9 0 3} & { 0. 0 7 3 1} \\ { 0. 0 9 4 5} & { 0. 0 8 8 5} & { - 0. 0 9 8 4} & { 0. 0 9 2 2} & { 0. 0 7 4 3} & { - 0. 0 8 1 5} & { 0. 0 9 9 8} & { - 0. 0 8 2 1} & { 0. 0 3 6 6} & { - 0. 0 7 2 3} \\ { - 0. 0 6 9 6} & { 0. 0 9 5 4} & { - 0. 0 9 3 7} & { 0. 0 9 2 0} & { - 0. 0 6 4 6} & { 0. 0 8 9 1} & { 0. 0 7 7 1} & { - 0. 0 9 6 1} & { 0. 0 8 6 6} & { 0. 0 5 6 7} \\ { 0. 0 9 7 5} & { - 0. 0 9 7 4} & { 0. 0 8 2 3} & { 0. 0 9 7 9} & { - 0. 0 3 6 7} & { - 0. 0 2 8 0} & { 0. 0 9 9 0} & { 0. 0 8 3 1} & { - 0. 0 8 4 9} & { 0. 0 9 6 1} \\ { 0. 0 7 9 5} & { - 0. 0 6 5 5} & { - 0. 0 9 2 9} & { 0. 0 9 3 2} & { - 0. 0 8 5 5} & { 0. 0 8 6 5} & { - 0. 0 8 0 3} & { 0. 0 9 9 1} & { 0. 0 5 8 4} & { 0. 0 8 8 2} \\ { 0. 0 9 6 6} & { - 0. 0 6 9 3} & { 0. 0 9 6 4} & { - 0. 0 9 5 5} & { 0. 0 8 0 3} & { 0. 0 3 1 1} & { 0. 0 8 1 6} & { 0. 0 5 9 9} & { 0. 0 9 1 4} & { 0. 0 9 8 1} \\ { 0. 0 9 2 8} & { - 0. 0 9 3 2} & { 0. 0 8 6 2} & { - 0. 0 6 7 6} & { - 0. 0 9 2 6} & { 0. 0 9 7 1} & { 0. 0 9 7 3} & { - 0. 0 3 4 1} & { 0. 0 9 9 9} & { 0. 0 8 1 2} \\ { - 0. 0 9 1 7} & { 0. 0 9 9 0} & { - 0. 0 4 1 8} & { - 0. 0 2 1 5} & { - 0. 0 8 4 1} & { 0. 0 8 9 2} & { 0. 0 9 1 5} & { - 0. 0 8 8 1} & { 0. 0 9 4 1} & { 0. 0 9 8 4} \\ { 0. 0 9 2 4} & { 0. 0 7 6 5} & { - 0. 0 9 6 6} & { 0. 0 9 7 2} & { 0. 0 7 5 7} & { - 0. 0 9 0 8} & { 0. 0 9 5 5} & { - 0. 0 4 2 0} & { - 0. 0 9 0 4} & { 0. 0 8 4 4} \\ { 0. 0 5 9 1} & { 0. 0 9 7 0} & { - 0. 0 7 6 2} & { 0. 0 8 1 3} & { 0. 0 6 1 8} & { 0. 0 8 6 5} & { 0. 0 9 6 5} & { - 0. 0 9 5 1} & { 0. 0 8 1 6} & { - 0. 0 9 5 3} \\ \end{array} } \right] $$

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Gao, X.Z., Ovaska, S.J., Wang, X. et al. Clonal optimization-based negative selection algorithm with applications in motor fault detection. Neural Comput & Applic 18, 719–729 (2009). https://doi.org/10.1007/s00521-009-0276-9

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