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Development of a dynamic maintenance system for electric motor’s failure prognosis

  • Mpinos Chr. Anastasios 
  • Karakatsanis S. Theoklitos 
Conference paper

Abstract

During the last years there have become a lot of studies concerning the preventive maintenance of mechanical equipment and fault diagnosis, each one of them having a different approach. The predictive maintenance allows to be continuously known the kind of maintenance and care that a system needs, and mainly what kind of equipment replacement is foreseen in the future. This paper presents the development of a smart – dynamic maintenance system for electric motor’s failure prognosis. The method is based on the analysis of the motor into his subsystems by using neural networks and by recording their relations and interactions. In this model are also embodied the possible damages of every subsystem and its symptoms, respectively. The objective goal of this analysis is to develop an algorithm for the calculation of the probability of showing the damage in a motor’s part according to its dynamic operational status. The creation of a data basis reflects the techniques experience concerning the causes and the possible preventive actions that are necessary. The suggested method is general and can be applied in every part or system of the mechanical equipment. Finally, the paper presents a simple study case for the rotor and practical conclusions are drawn which can lead to a better focus on preventive maintenance.

Keywords

Electric Motor Fault Diagnosis Preventive Maintenance Spare Part Mechanical Equipment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    J. D. Patton, “Preventive Maintenance”: Instrument Soc. Of America, 1983.Google Scholar
  2. 2.
    S.S. Rao, “Reliability-Based Design”: McGraw-Hill, 1992.Google Scholar
  3. 3.
    D. Chelidze and J. Cusumano, “A dynamical systems approach to failure prognosis,” J. Vib. Acoust., vol. 126, no. 1, pp. 1–7, 2004CrossRefGoogle Scholar
  4. 4.
    W. Wang, F. Golnaraghi, and F. Ismail, “Condition monitoring of a multistage printing press,” J. Sound Vib., vol. 270, no. 5-6, pp. 755–766, 2004.CrossRefGoogle Scholar
  5. 5.
    W. Wang, F. Ismail, and F. Golnaraghi, “A neuro-fuzzy approach for gear system monitoring,” IEEE Trans. Fuzzy Syst., vol. 12, no. 5, pp. 710–723, Oct. 2004.CrossRefGoogle Scholar
  6. 6.
    S. K. Yang and T. S. Liu, “A petri net approach to early failure detection and isolation for preventive maintenance”, Qual. Reliab. Eng. Int., vol.14, pp 319-330, 1998.CrossRefGoogle Scholar
  7. 7.
    C. Li and H. Lee, “Gear fatigue crack prognosis using embedded model gear dynamic model and fracture mechanics,” Mech. Syst. Signal Process., vol. 20, pp. 836–846, 2005.CrossRefGoogle Scholar
  8. 8.
    P. McFadden and J. Smith, “Vibration monitoring of rolling element bearings by the high frequency resonance technique—A review,” Tribology Int., vol. 17, no. 1, pp. 3–10, 1984.CrossRefGoogle Scholar
  9. 9.
    N. Tandon and A. Choudhury, “Areviewof vibration and acoustic measurement methods for the detection of defects in rolling element bearings,” Tribology Int., vol. 32, pp. 469–480, 1999.CrossRefGoogle Scholar
  10. 10.
    J. Jang, C. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing. Englewood Cliffs, NJ: Prentice-Hall, 1997.Google Scholar
  11. 11.
    W. Wang, F. Golnaraghi, and F. Ismail, “Prognosis of machine health condition using neuro-fuzzy systems,” Mech. Systems Signal Process., vol. 18, no. 4, pp. 813–831, 2004.CrossRefGoogle Scholar
  12. 12.
    An Intelligent System for Machinery Condition Monitoring IEEE Trans. Fuzzy Syst 10.1109/TFUZZ.2007.896237 January 17, 2006.Google Scholar
  13. 13.
    R. Patton, P. Frank, and R. Clark, Issues of Fault Diagnosis for DynamicSystems. New York: Springer-Verlag, 2000.Google Scholar
  14. 14.
    J. Korbicz, Fault Diagnosis: Models, Artificial Intelligence, Applications. New York: Springer-Verlag, 2004.MATHGoogle Scholar
  15. 15.
    M. Pourahmadi, Foundation of Time Series Analysis and Prediction Theory. New York: Wiley, 2001.Google Scholar
  16. 16.
    J. Korbicz, Fault Diagnosis: Models, Artificial Intelligence, Applications. New York: Springer-Verlag, 2004.Milan J & Munt CE. (1992)Google Scholar
  17. 17.
    F. Zhao, X. Koutsoukos, H. Haussecker, J. Reich, and P. Cheung, “Monitoring and fault diagnosis of hybrid systems,” IEEE Trans. Syst.,Man, Cybern. B, Cybern., vol. 35, no. 6, pp. 1225–1240, Dec. 2005.CrossRefGoogle Scholar
  18. 18.
    J. Gusumano, D. Chelidze, and A. Chatterjee, “Dynamical systems approach to damage evolution tracking, Part 2: Model-based validation and physical interpretation,” J. Vibrat. Acoust., vol. 124, no. 2, pp. 258–264, 2002.CrossRefGoogle Scholar
  19. 19.
    Y. Murphey, J. Crossman, Z. Chen, and J. Cardillo, “Automotive fault diagnosis—Part II:Adistributed agent diagnostic system,” IEEE Trans. Veh. Technol., vol. 52, no. 4, pp. 1076–1098, Jul. 2003.CrossRefGoogle Scholar
  20. 20.
    I. Rish, M. Brodie, S. Ma,N. Odintsova,A. Beygelzimer,G. Grabarnik, and K. Hernandez, “Adaptive diagnosis in distributed systems,” IEEE Trans. Neural Netw., vol. 16, no. 5, pp. 1088–1109, Sep. 2005.CrossRefGoogle Scholar
  21. 21.
    D. Quinn, G. Mani, M. Kasarda, T. Bash, D. Inman, and R. Kirk, “Damage detection of a rotating cracked shaft using an active magnetic bearing as a force actuator-analysis and experimental verification,” IEEE/ASME Trans. Mechatronics, vol. 10, no. 6, pp. 640–647, Dec. 2005.CrossRefGoogle Scholar
  22. 22.
    H. Ishibuchi and T. Nakashima, “Effect of rule weights in fuzzy rulebased classification systems,” IEEE Trans. Fuzzy Syst., vol. 9, no. 4, pp. 506–515, Aug. 2001. [12] M. Kowal and J. Korbicz, “Robust fault detection using neuro-fuzzy networks,” in Proc. 16th IFAC World Congr., 2004, Prague Czech Republic, CD-ROM.Google Scholar
  23. 23.
    J. Wang and C. Lee, “Self-adaptive neuro-fuzzy inference systems for classification applications,” IEEE Trans. Fuzzy Syst., vol. 10, no. 6, pp. 790–802, Dec. 2002.CrossRefGoogle Scholar
  24. 24.
    W. Wang, “An adaptive predictor for dynamic system forecasting,” Mech. Syst. Signal Process., vol. 21, no. 2, pp. 809–823, 2007.CrossRefGoogle Scholar
  25. 25.
    W. Wang, F. Ismail, and F. Golnaraghi, “Assessment of gear damage monitoring techniques using vibration measurements,” Mech. Syst. Signal Process., vol. 15, no. 5, pp. 905–922, 2001.CrossRefGoogle Scholar
  26. 26.
    Y. Li, T. Kurfess, and S. Liang, “Stochastic prognostics for rolling element bearings,” Mech. Syst. Signal Process., vol. 14, no. 5, pp. 737–762, 2000.CrossRefGoogle Scholar
  27. 27.
    P. Tse and D. Atherton, “Prediction of machine deterioration using vibration based fault trends and recurrent neural networks,” J. Vib. Acoust., vol. 121, no. 3, pp. 355–362, 1999.CrossRefGoogle Scholar
  28. 28.
    A. Ray and S. Tangirala, “Stochastic modeling of fatigue crack dynamics for online failure prognostics,” IEEE Trans. Control Syst. Technol., vol. 4, no. 4, pp. 443–451, Jul. 1996.CrossRefGoogle Scholar
  29. 29.
    A. Atiya, S. El-Shoura, S. Shaheen, and M. El-Sherif, “A comparison between neural-network forecasting techniques— Case study: River flow forecasting,” IEEE Trans. Neural Netw., vol. 10, no. 2, pp. 402–409, Mar. 1999.CrossRefGoogle Scholar
  30. 30.
    G. Corani and G. Guariso, “Coupling fuzzy modeling and neural networks for river flood prediction,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 35, no. 3, pp. 382–390, Aug. 2005.CrossRefGoogle Scholar
  31. 31.
    V. Giurgiutiu, “Current issues in vibration-based fault diagnostics and prognostics,” in Proc. SPIE 9th Int. Symp. Smart Structures Materials, San Diego, CA, 2002, pp. 17–21.Google Scholar
  32. 32.
    D. McFadden, “Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration,” J. Vib., Acoust., Stress, Reliab. Design, vol. 108, pp. 165–170, 1986.Google Scholar
  33. 33.
    N. Nikolaou and I. Antoniadis, “Rolling element bearing fault diagnosis using wavelet packets,” Nondestructive Test. Eval. Int., vol. 35, pp. 197–205, 2002.Google Scholar
  34. 34.
    M. Figueiredo, R. Ballini, S. Soares, M. Andrade, and F. Gomide, “Learning algorithms for a class of neurofuzzy network and applications,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 34, no. 3, pp. 293–301, Aug. 2004.CrossRefGoogle Scholar
  35. 35.
    D. Nauck, “Adaptive rule weights in neuro-fuzzy systems,” Neural Comput. Appl., vol. 9, pp. 60–70, 2000.CrossRefGoogle Scholar
  36. 36.
    H. Ishibuchi and T. Yamamoto, “Rule weight specification in fuzzy rule-based classification systems,” IEEE Trans. Fuzzy Syst., vol. 13, no. 4, pp. 428–435, Oct. 2005.CrossRefGoogle Scholar
  37. 37.
    Statistical analysis. SKF, Gerard Schram, May 2003.Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Mpinos Chr. Anastasios 
    • 1
  • Karakatsanis S. Theoklitos 
    • 1
  1. 1.Democritus University of ThraceXanthiGreece

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