Skip to main content
SpringerLink
Log in

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

  • Conference paper
Engineering Asset Lifecycle Management

  • 3265 Accesses

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. J. D. Patton, “Preventive Maintenance”: Instrument Soc. Of America, 1983.

    Google Scholar 

  2. S.S. Rao, “Reliability-Based Design”: McGraw-Hill, 1992.

    Google Scholar 

  3. D. Chelidze and J. Cusumano, “A dynamical systems approach to failure prognosis,” J. Vib. Acoust., vol. 126, no. 1, pp. 1–7, 2004

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  10. J. Jang, C. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing. Englewood Cliffs, NJ: Prentice-Hall, 1997.

    Google Scholar 

  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.

    Article  Google Scholar 

  12. An Intelligent System for Machinery Condition Monitoring IEEE Trans. Fuzzy Syst 10.1109/TFUZZ.2007.896237 January 17, 2006.

    Google Scholar 

  13. R. Patton, P. Frank, and R. Clark, Issues of Fault Diagnosis for DynamicSystems. New York: Springer-Verlag, 2000.

    Google Scholar 

  14. J. Korbicz, Fault Diagnosis: Models, Artificial Intelligence, Applications. New York: Springer-Verlag, 2004.

    MATH  Google Scholar 

  15. M. Pourahmadi, Foundation of Time Series Analysis and Prediction Theory. New York: Wiley, 2001.

    Google Scholar 

  16. J. Korbicz, Fault Diagnosis: Models, Artificial Intelligence, Applications. New York: Springer-Verlag, 2004.Milan J & Munt CE. (1992)

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  24. W. Wang, “An adaptive predictor for dynamic system forecasting,” Mech. Syst. Signal Process., vol. 21, no. 2, pp. 809–823, 2007.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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. 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. 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.

    Article  Google Scholar 

  35. D. Nauck, “Adaptive rule weights in neuro-fuzzy systems,” Neural Comput. Appl., vol. 9, pp. 60–70, 2000.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  37. Statistical analysis. SKF, Gerard Schram, May 2003.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Chr. Anastasios Mpinos & S. Theoklitos Karakatsanis

Authors
  1. Chr. Anastasios Mpinos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Theoklitos Karakatsanis
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Dimitris Kiritsis

  2. C.E.T.I/R.C. Athena, Athens, Greece

    Christos Emmanouilidis

  3. CIEAM, University of South Australia, Adelaide, Australia

    Andy Koronios

  4. CRC for Integrated Engineering Asset Management (CIEAM), Australia

    Joseph Mathew

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag

About this paper

Cite this paper

Mpinos, C., Karakatsanis, S. (2010). Development of a dynamic maintenance system for electric motor’s failure prognosis. In: Kiritsis, D., Emmanouilidis, C., Koronios, A., Mathew, J. (eds) Engineering Asset Lifecycle Management. Springer, London. https://doi.org/10.1007/978-0-85729-320-6_97

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-320-6_97

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-321-3

  • Online ISBN: 978-0-85729-320-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation