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Experimental vibratory analysis of a fan motor in industrial environment

  • Tarek Kebabsa
  • Nouredine Ouelaa
  • Abderrazek Djebala
ORIGINAL ARTICLE
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Abstract

In this study, we present an application on the use of spectral analysis as aid to diagnosis and decision-making before a failure occurs due to a bad monitoring. The work is done on a strategic machine of a largest industrial company. This paper proposes the use of a new indicator that leads viewing the status of the machine before making an intervention and then exploiting the results as a handy reference. These values show the true state of the machine in terms of vibration diagnosis. The application of these decision-making references to a fan motor and its inclusion in the scorecard maintenance avoids unnecessary repairs caused by random decisions. The objective is to increase the life of the equipment and reduce the cost of production, resulting in the improvement of parameters such as the average level of functioning in an e-maintenance system. The comparison of the machine reliability before detecting the anomaly and the machine reliability after the proposal to use the new indicator and the intervention of the maintenance shows a best amelioration.

Keywords

Spectral analysis Fault diagnosis NG measured and calculated NG average Conditional preventive maintenance Fan motor 

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References

  1. 1.
    Boulenger A, Pachaud C et al (1998) Vibratory diagnosis in maintenance préventive. ISBN 2100041053. Dunod, Paris, pp 239–295Google Scholar
  2. 2.
    Hang J (2002) Practice preventive maintenance, mechanical, pneumatic, hydraulic, electrical, cold. Paris. ISBN 210 00 6561 0Google Scholar
  3. 3.
    Estoq P (2004) A methodological approach numerical and experimental support for the detection and monitoring of vibration fault chipping ball bearing. PHD ThesisGoogle Scholar
  4. 4.
    Vibro-Meter (1991) the rotating machinery and vibration behavior P / N 561–003 FGoogle Scholar
  5. 5.
    Yang S, Xiaoli L, Ming L (2011) Bearing condition monitoring and fault diagnosis of a wind turbine using parameter free detection. Journal springer link, LENN 100:289–294Google Scholar
  6. 6.
    Galard S, Berges L, Royou L (2009) Overall levels of vibration factory the need for global indicators in CBM. Proceedings of COMADEM, San Sebastian, Spain. 9–11 JuneGoogle Scholar
  7. 7.
    Muller A (2005) Contribution à la maintenance prévisionnelle des systèmes de production par la formalisation d’un pronostic. Thèse de Doctorat, Ecole doctorale IAEM, Lorraine, FranceGoogle Scholar
  8. 8.
    Ben Salem A (2008) Modèles probabilistes de séquences temporelles et fusion de décisions. Application à la classification de défauts de rails et à leur maintenance Thèse de Doctorat, Ecole doctorale IAEM, LorraieneGoogle Scholar
  9. 9.
    Lin J, Zuo MJ (2003) Gearbox fault diagnosis using adaptive wavelet filter. Mech Syst Signal Process 17:1259–1269CrossRefGoogle Scholar
  10. 10.
    Sidahmed M (1990) Détection précoce de défauts dans les engrenages par analyse vibratoire. Revue Française de Mécanique 4:243–254Google Scholar
  11. 11.
    Dalpiaz G, Rivola A Rubini R. Gear fault monitoring, comparison of vibration analysis techniques DIEM, University of bologna, ItalyGoogle Scholar
  12. 12.
    Capdessus C, Sidahmed M (1991) Analyse des vibrations d’un engrenage : cepstre, corrélation, spectre. Traitement du signal 5(8):365–372Google Scholar
  13. 13.
    Cousinard O, Rousseau P, Bolaers F, et Marconnet P (2004) Paramétrage, utilisation et apport de l'analyse cepstrale en maintenance prévisionnelle. Méc Ind 5:393–406CrossRefGoogle Scholar
  14. 14.
    Pachaud C, Salvetas R, Fray C (1997) Crest factor and kurtosis contributions to identify defects inducing periodical impulsive process. Mach Syst Signal Process 11:903–916CrossRefGoogle Scholar
  15. 15.
    Djebala A, Ouelaa N, Benchaabane C, Laefer DF (2012) Application of the wavelet multi-resolution analysis and Hilbert transform for the prediction of gear tooth defects. Meccanica 47(7):1601–1612CrossRefzbMATHGoogle Scholar
  16. 16.
    Chinmaya K, Mohanty AR (2006) Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mach Syst Signal Process 20:158–187CrossRefGoogle Scholar
  17. 17.
    Gerhard P (2004) Analysis of practical vibration machines that predictive maintenance. Elsevier, OxfordGoogle Scholar
  18. 18.
    Galar D, Pilar L, Luis B (2011) Application of dynamic benchmarking of rotating machinery for e-maintenance. Lulea University of Technology, Journal springer link, 246–262, Lulea, SwedenGoogle Scholar
  19. 19.
    Ahn J-H, Kwak D-H, Koh B-H (2014) Fault detection of a roller-bearing system through the EMD of a wavelet denoised signal. Sensors 14(8):15022–15038CrossRefGoogle Scholar
  20. 20.
    Kedadouche M, Marc T, Antoine T (2014) Monitoring machines by using a hybrid method combining MED, EMD, and TKEO. Adv Acoust Vib 2014:1–10Google Scholar
  21. 21.
    Djebala A, Babouri MK, Ouelaa N (2015) Rolling bearing fault detection using a hybrid method based on empirical mode decomposition and optimized wavelet multi-resolution analysis. Int J Adv Manuf Technol 79(9–12):2093–2105CrossRefGoogle Scholar
  22. 22.
    Bouhalais M, Djebala A, Ouelaa N, Babouri MK (2018) CEEMDAN and OWMRA as a hybrid method for rolling bearing fault diagnosis under variable speed. Int J Adv Manuf Technol 94(5–8):2475–2489CrossRefGoogle Scholar
  23. 23.
    Capdessus C, Edgard S, Jérôme A (2014) Speed transform, a new time-varying frequency analysis technique. Advances in condition monitoring of machinery in non-stationary operations. Springer, Berlin, pp 23–35CrossRefGoogle Scholar
  24. 24.
    Khalid AS et al (2013) Vibratory monitoring of a spalling bearing defect in variable speed regime. Mech Ind 14(2):129–136MathSciNetCrossRefGoogle Scholar
  25. 25.
    Wu TY, Lai CH, Liu DC (2016) Defect diagnostics of roller bearing using instantaneous frequency normalization under fluctuant rotating speed. J Mech Sci Technol 30(3):1037–1048CrossRefGoogle Scholar
  26. 26.
    Kebabsa T, Ouelaa N, Jérôme A et al (2015) Experimental study of a turbo-alternator in industrial environment using cyclostationarity analysis. Int J Adv Manuf Technol 78(5–8):0268–3768Google Scholar
  27. 27.
    Shenk (1999) Manual on Operating VIBROTEST 60. Enterprise fertialGoogle Scholar
  28. 28.
    Overview (1974) of the ISO 2372 international standard ISO 10816 (1995), replaces the standard ISO 2372Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Tarek Kebabsa
    • 1
    • 2
  • Nouredine Ouelaa
    • 2
  • Abderrazek Djebala
    • 2
  1. 1.Higher School of Industrial TechnologyAnnabaAlgeria
  2. 2.Mechanics & Structures LaboratoryGuelmaAlgeria

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