Skip to main content

Advertisement

SpringerLink
Log in

Fault Detection, Diagnosis and Prediction in Electrical Valves Using Self-Organizing Maps

  • Published:
Journal of Electronic Testing Aims and scope Submit manuscript

Abstract

This paper presents a proactive maintenance scheme for fault detection, diagnosis and prediction in electrical valves. The proposed scheme is validated with a case study, considering a specific valve used for controlling the oil flow in a distribution network. The scheme is based in self-organizing maps, which perform fault detection and diagnosis, and temporal self-organizing maps for fault prediction. The adopted fault model considers deviations either in torque, in the valve’s gate position or in the opening or closing time. The map which performs the fault detection, diagnosis and prediction, is trained with the energy spectral density information, obtained from the torque and position signals by applying the wavelet packet transform. These signals are provided by a mathematical model devised for the electrical valve. The training is performed by fault injection based on parameter deviations over this same mathematical model. The proposed system is embedded into an FPGA-based platform. Experimental results demonstrate the effectiveness of the proposed approaches.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Araújo AFR, Barreto GA (2002) Context in temporal sequence processing: a self-organizing approach and its application to robotics. IEEE Trans Neural Netw 13(1):45–57

    Article  Google Scholar 

  2. Djurdjanovic D, Lee J, Ni J (2003) Watchdog agent—an infotronics based prognostic approach for product performance degradation assessment and prediction. Adv Eng Inf 17(5):109–125

    Article  Google Scholar 

  3. Eren L, Devaney MJ (2004) Bearing damage detection via wavelet packet decomposition of the stator current. IEEE Trans Instrum Meas 53(2):431–436

    Article  Google Scholar 

  4. Fitzgerald AE (1990) Electric machinery. McGraw-Hill, New York, 703 pp

    Google Scholar 

  5. Frank RJ, Davey N, Hunt S (2001) Time series prediction and neural networks. J Intell Robot Syst 31(1):91–103

    Article  MATH  Google Scholar 

  6. Guleryuz OG (2009) Wavelet packets source codes. Polytechnic Institute of New York University. Available at: http://eeweb.poly.edu/onur/source.html

  7. Hammer B, Micheli A, Neubauer N, Sperduti A, Strickert M. (2005) Self-organizing maps for time series. In: Proceedings of workshop on self-organizing maps, vol 1, pp 115–122

  8. Huang R, Xi L, Li X, Liu CR, Qiu H, Lee J (2005) Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. J Mech Syst Signal Process 21(1):193–207

    Article  Google Scholar 

  9. IEEE (1994) IEEE standard glossary of computer hardware terminology. Institute of Electrical and Electronics Engineers, Nova York, 109 pp

    Google Scholar 

  10. Jinhua D, Erland O (2002) Availability analysis through relations between failure rate and preventive maintenance under condition monitoring. Department of Information Design and Product Development, Mälardalen University, Sweden, 21

  11. Khoshgoftaar TM, Seliya N (2003) Fault prediction modeling for software quality estimation: comparing commonly used techniques. J Empir Softw Eng 8(3):255–283

    Article  Google Scholar 

  12. Kohonen T (1997) Self-organizing maps. In: Series in information sciences, 2nd edn. Springer, Heidelberg (1997)

    Google Scholar 

  13. Koskela T, Varsta M, Heikkonen J, Kaski K (1998) Temporal sequence processing using recurrent SOM. In: 2nd international conference on knowledge-based intelligent electronic systems, Adelaide, Australia, vol 1(21), pp 290–297

  14. Kundur P (1994) Power system stability and control. McGraw-Hill, New York, 1176 pp

    Google Scholar 

  15. Lathi BP (1998) Modern digital and analog communication systems. Oxford University, New York, 800 pp

    Google Scholar 

  16. Liu Y, Weisberg RH, Shay LK (2007) Current patterns on the west Florida shelf from joint self-organizing map analyses of HF radar and ADCP data. J Atmos Ocean Technol (Am Meteorol Soc) 24(4):702–712

    Article  Google Scholar 

  17. Matos MC, Osório PLM, Johann PRS (2007) Unsupervised seismic pattern recognition using wavelet transform and self-organizing maps. J Geophys 72(1):9–21

    Google Scholar 

  18. Patan K, Korbicz J (2000) Application of dynamic neural networks in an industrial plant. In: Proceedings of 4th IFAC symposium on fault detection supervision and safety for technical processes, Budapeste, Hungria, vol 1(1), pp 186–191

  19. Qiu H, Lee J, Lin J, Yu G (2006) Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. J Sound Vib 289(4):1066–1090

    Article  Google Scholar 

  20. Randall RB (2004) Detection and diagnosis of incipient bearing failure in helicopter gearboxes. J Eng Failure Anal 11(2):177–190

    Article  MathSciNet  Google Scholar 

  21. Saxena A, Saad A (2007) Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. J Appl Soft Comput (Elsevier) 7(1):441–454

    Google Scholar 

  22. Senjyu T, Tamaki Y, Uezato K (2000) Current signature analysis of induction motor mechanical faults by wavelet packet decomposition. In: Proceedings of international conference on power system technology, vol 2(1), pp 1113–1118

  23. Simon G, Lendasse A, Cottrell M, Fort JC, Verleysen M (2004) Double quantization of the regressor space for long-term time series prediction: method and proof of stability. J Neural Netw 17(1):1169–1181

    Article  MATH  Google Scholar 

  24. Singh GK, Kazza SAS (2003) Induction machine drive condition monitoring and diagnostic research—a survey. J Electr Power Syst Res 64(2):145–158

    Article  Google Scholar 

  25. Tan WW, Huo H (2005) A generic neurofuzzy model-based approach for detecting faults in induction motors. IEEE Trans Ind Electron 52(5):1420–1427

    Article  Google Scholar 

  26. Tinós R, Terra M H (2001) Fault detection and isolation in robotic manipulators using a multilayer perceptron and a RBF network trained by the Kohonen’s self-organizing map. Rev Soc Bras Autom Contr Autom 12(1):11–18

    Google Scholar 

  27. Varsta M, Heikkonen J, Lampinen J, Millán JR (2001) Temporal kohonen map and the recurrent self-organizing map: analytical and experimental comparison. Proc Neural Process Lett 13(1):237–251

    Article  MATH  Google Scholar 

  28. Vesanto J (1997) Using the SOM and local models in time-series prediction. In: Proceedings of the workshop on self-organizing maps, Finland, pp 209–214

  29. Voegtlin T (2002) Recursive self-organizing maps. Proc Neural Netw 15(8):979–991

    Article  Google Scholar 

  30. Xilinx (2005) Embedded system tools reference manual. Embedded Development Kit EDK 8.1i, Xilinx Inc

  31. Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678

    Article  Google Scholar 

  32. Yam RCM, Tse PW, Ling L, Tu P (2001) Intelligent predictive decision support system for condition-based maintenance. J Adv Manuf Technol 17(5):383–391

    Article  Google Scholar 

  33. Yang BS, Han T, An JL (2004) Art–Kohonen neural network for fault diagnosis of rotating machinery. J Mech Syst Signal Process 18(3):645–657

    Article  Google Scholar 

  34. Yan J, Lee J (2005) Degradation assessment and fault modes classification using logistic regression. J Manuf Sci Eng 127:912–914

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the CNPq Brazilian Research Agency under contract number 142027/2008-1, CAPES Brazilian Research Agency and Petrobras S.A.

Author information

Authors and Affiliations

  1. Departamento de Engenharia Elétrica, Universidade Federal do Rio Grande do Sul, Av. Osvaldo Aranha, 103, Bom Fim, CEP: 90.035-190, Porto Alegre, RS, Brasil

    Luiz Fernando Gonçalves, Tiago Roberto Balen, Marcelo Soares Lubaszewski, Eduardo Luis Schneider & Renato Ventura Henriques

  2. Instituto de Informática, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500, Agronomia, CEP: 91.509-900, Porto Alegre, RS, Brasil

    Jefferson Luiz Bosa

Authors
  1. Luiz Fernando Gonçalves
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jefferson Luiz Bosa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tiago Roberto Balen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marcelo Soares Lubaszewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eduardo Luis Schneider
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Renato Ventura Henriques
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luiz Fernando Gonçalves.

Additional information

Responsible Editor: F. Vargas

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gonçalves, L.F., Bosa, J.L., Balen, T.R. et al. Fault Detection, Diagnosis and Prediction in Electrical Valves Using Self-Organizing Maps. J Electron Test 27, 551–564 (2011). https://doi.org/10.1007/s10836-011-5220-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10836-011-5220-0

Keywords

Search

Navigation