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Estimation of rolling bearing life with damage curve approach

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The ability to determine the bearing life time is one of the main purposes in maintenance of rotating machineries. Because of reliability, cost and productivity, the bearing life time prognostic is important. In this paper, a stiffness-based prognostic model for bearing systems is discussed. According to presumed model of bearing and fundamental of damage mechanics, damage curve approach is used to relate stiffness of vibratory system and bearing running life. Furthermore, using the relation between acceleration amplitude at natural frequency and stiffness, final relation between acceleration amplitude at natural frequency and running life time according to damage curve approach can be established and the final running time is predicted. Experiments have been performed on self alignment bearing under failures on inner race and outer race to calibrate and to validate the proposed model. The comparison between model-calculated data and experimental results indicates that this model can be used effectively to predict the failure lifetime and the remaining life of a bearing system.
Rocznik
Tom
Strony
66--70
Opis fizyczny
Bibliogr. 12 poz., rys.
Twórcy
autor
  • Depatment of Mechanical Engineering, Sharif University of Technology 11155-9567, Azadi Avenue, Tehran, IRAN, m_behzad@sharif.edu
Bibliografia
  • 1. Qui H., Lee J., Yu G.: Robust performance degradation assessment methods for enhanced rolling element bearing prognostics, Advanced Engineering Informatics, Vol. 17 No. (3-4), pp.127-140, 2003
  • 2. Zhao Y., Zhang G., Du J., Wang G., Vachsevanos G.: Development of distributed bearing health monitoring and assessing system, 8th International conference on control, Automation, Robotics and Vision, pp. 474-478, 2004
  • 3. Mba D., Al-Ghamd A.M.: A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size, Journal of mechanical systems and signal processing, Vol. 20 No. 7, pp. 1537-1571, 2006
  • 4. Da silva V., Fujimoto R.Y., Padovese L.R.: Rolling bearing fault diagnostic system using fuzzy logic, 10th IEEE International Conference on Fuzzy systems, Vol 3 No. 3, pp. 816-819, 2001
  • 5. Artes M., Del Castillo L. and Perez J.: Failure prevention and diagnosis in machine elements using cluster, proceeding of the tenth international congress on sound and vibration, pp. 1197- 1203, 2003
  • 6. Gebraeel N., Lawley M., Liu R., Parmeshwaran V.: Residual life predictions from vibration-based degradation signals: a neural network approach, IEEE Transactions on Industrial Electronics, Vol. 51 No. 3, pp. 694-700, 2004
  • 7. Liu T., Ordukhani F., Dipak J.: Monitoring and diagnosis of roller bearing conditions using neural networks and soft computing, International journal of knowledge-based and intelligent engineering systems, Vol. 9 No. 2, pp. 149-157, 2005
  • 8. Li Y., Billington S., Zhang C., Kurfess T., Danyluk S., Liang S.: Adaptive prognostics for rolling element bearing condition, Journal of mechanical systems and signal processing, Vol. 13 No. 1, pp. 103-113, 1999
  • 9. Li Y., Billington S., Zhang C., Kurfess T., Danyluk S., Liang S.: Dynamic prognostic prediction of defect propagation on rolling element bearings, Tribology Transactions, Vol. 42 No. 2, pp. 385-392, 1999
  • 10. Qiu J., Seth B. B., Liang S., Zhang C.: Damage mechanics approach for bearing lifetime prognostics, Journal of mechanical systems and signal processing, Vol. 16 No. 5, pp. 817-829, 2002
  • 11. Li Y., Kurfess T., Liang S.: Stochastic prognostics for rolling element bearings, Journal of mechanical systems and signal processing, Vol. 14 No. 5, pp. 747-762, 2000
  • 12. Lemaitre J., Desmorat R.: Engineering Damage Mechanics, Springer-verlag, Berlin, 2005.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWM4-0033-0050
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