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A neural networks-based negative selection algorithm in fault diagnosis

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Abstract

Inspired by the self/nonself discrimination theory of the natural immune system, the negative selection algorithm (NSA) is an emerging computational intelligence method. Generally, detectors in the original NSA are first generated in a random manner. However, those detectors matching the self samples are eliminated thereafter. The remaining detectors can therefore be employed to detect any anomaly. Unfortunately, conventional NSA detectors are not adaptive for dealing with time-varying circumstances. In the present paper, a novel neural networks-based NSA is proposed. The principle and structure of this NSA are discussed, and its training algorithm is derived. Taking advantage of efficient neural networks training, it has the distinguishing capability of adaptation, which is well suited for handling dynamical problems. A fault diagnosis scheme using the new NSA is also introduced. Two illustrative simulation examples of anomaly detection in chaotic time series and inner raceway fault diagnosis of motor bearings demonstrate the efficiency of the proposed neural networks-based NSA.

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References

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

    MATH  Google Scholar 

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

  3. Garrett SM (2005) How do we evaluate artificial immune systems?. Evol Comput 13(2):145–178

    Article  Google Scholar 

  4. Dasgupta D (2006) Advances in artificial immune systems. IEEE Comput Intell Mag 1(4):40–49

    Google Scholar 

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

  6. Stibor T, Mohr P, Timmis J, Eckert C (2005) Is negative selection appropriate for anomaly detection? In: Proceedings of the genetic and evolutionary computation conference, Washington D.C., pp 321–328

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

    Article  Google Scholar 

  8. Dasgupta D, Forrest S (1995) Tool breakage detection in milling operations using a negative selection algorithm. Technical Report CS95-5, Department of Computer Science, University of New Mexico, Albuquerque, NM

  9. Dasgupta D, KrishnaKumar K, Wong D, Berry M (2004) Negative selection algorithm for aircraft fault detection. In: Proceedings of the 3rd international conference on artificial immune systems, Catania, Sicily, Italy, pp 1–13

  10. Ji Z, Dasgupta D (2006) Applicability issues of the real-valued negative selection algorithms. In: Proceedings of the genetic and evolutionary computation conference, Seattle, WA, pp 111–118

  11. González F (2003) A study of artificial immune systems applied to anomaly detection. Ph.D. Dissertation, Division of Computer Science, University of Memphis, Memphis, TN

  12. 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, UK, pp 261–272

  13. Stibor T, Timmis J, Eckert C (2005) A comparative study of real-valued negative selection to statistical anomaly detection techniques. In: Proceedings of the 4th international conference on artificial immune systems, Banff, Alberta, Canada, pp 262–275

  14. Haykin S (1998) Neural networks, a comprehensive foundation, 2nd edn. Prentice-Hall, Upper Saddle River

  15. 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

    Article  MATH  Google Scholar 

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

  17. Mackey MC, Glass L (1977) Oscillation and chaos in physiological control systems. Science 197:287–289

    Article  Google Scholar 

  18. 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

    Article  Google Scholar 

  19. Gao XZ, Ovaska SJ, Wang X, Chow MY (2006) Clonal optimization of negative selection algorithm with applications in motor fault detection. In: Proceedings of the IEEE international conference on systems, man, and cybernetics, Taipei, Taiwan, pp 5118–5123

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Acknowledgments

This research work was funded by the Academy of Finland under Grant 214144. 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. Institute of Intelligent Power Electronics, 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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Correspondence to X. Z. Gao.

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Gao, X.Z., Ovaska, S.J., Wang, X. et al. A neural networks-based negative selection algorithm in fault diagnosis. Neural Comput & Applic 17, 91–98 (2008). https://doi.org/10.1007/s00521-007-0092-z

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  • DOI: https://doi.org/10.1007/s00521-007-0092-z

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