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Bearing Failure Analysis Using Vibration Analysis and Natural Frequency Excitation

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Abstract

Ball bearings are the most critical components of rotating machinery in oil and gas companies. Typical research has focused on bearing failure detection based on bearing failure frequencies derived from the velocity spectrum. However, most bearing failures are caused by improper or insufficient lubrication. The current research utilizes a case study demonstrating when ball bearings must be replaced or relubricated due to poor lubrication conditions. Poor lubrication is the cause of natural frequency excitation in bearings, where rapid bearing damage is typically induced by poor lubrication film. According to experimental data in this study, the bearing failed due to natural frequency excitation. In addition, when analyzing a signal with the velocity spectrum, high frequencies are displayed. Bearing failure is detected without bearing failure frequencies using the natural frequencies of the bearing in the velocity spectrum signal. Moreover, an experimental investigation of the bearing failure of a liquid ring compressor was conducted utilizing a VIBXPERT II vibration analyzer and the Omni trend software. The velocity spectrum is derived based on a fast Fourier transform from a time signal. After lubricating natural frequencies must be disappeared from the velocity spectrum otherwise, the bearing is failed and must be changed.

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References

  1. R. Rohani, S.M. Jafari, M. Roozban, Study of ball bearings failure modes in an eddy current dynamometer. J. Eng. Res. 41, 03–11 (2016)

    Google Scholar 

  2. A. Hemati, A case study: fluting failure analysis by using vibrations analysis. J. Fail. Anal. Prev. 19, 917–921 (2019)

    Article  Google Scholar 

  3. P. Zhou, M. Lin, F. Zhou, L. Gong, W. Ao, Bearing characteristics and failure mechanism of a novel plate-short anchor structure for tunnel crack reinforcement. Eng. Fail. Anal. 135, 106–160 (2022)

    Article  Google Scholar 

  4. N.W. Nirwan, H.B. Ramani, Condition monitoring and fault detection in roller bearing used in rolling mill by acoustic emission and vibration analysis. Mater. Today Proc. 51, 344–354 (2022)

    Article  Google Scholar 

  5. H. Nakamura, Y. Mizuno, Diagnosis for slight bearing fault in induction motor based on combination of selective features and machine learning. Energies. 15, 453 (2022)

    Article  Google Scholar 

  6. Y. Liu, Z. Chen, K. Wang et al. Surface wear evolution of traction motor bearings in vibration environment of a locomotive during operation. Sci. China Technol. Sci. 65, 920–931 (2022)

    Article  Google Scholar 

  7. S. Pattabhiraman, G. Levesque, N.H. Kim, N.K. Arakere, Uncertainty analysis for rolling contact fatigue failure probability of silicon nitride ball bearings. Int. J. Solids Struct. 47, 2543–2553 (2010)

    Article  CAS  Google Scholar 

  8. A. Dhanola, H.C. Garg, Tribological challenges and advancements in wind turbine bearings: a review. Eng. Fail. Anal. 118, 104885 (2020)

    Article  Google Scholar 

  9. R.G. Desavale, R. Venkatachalam, S.P. Chavan, Antifriction bearings damage analysis using experimental data based models. J. Tribol. 135(4), 041105 (2013)

    Article  Google Scholar 

  10. I.M. Jamadar, D.P. Vakharia, An in-situ synthesized model for detection of defective roller in rolling bearings. Eng. Sci. Technol. Int. J. 19(3), 1488–1496 (2016)

    Google Scholar 

  11. A. Khadersab, S. Shivakumar, Vibration analysis techniques for rotating machinery and its effect on bearing faults. Proc. Manuf. 20, 247–252 (2018)

    Google Scholar 

  12. L. Bizarre, F. Nonato, K.L. Cavalca, Formulation of five degrees of freedom ball bearing model accounting for the nonlinear stiffness and damping of elastohydrodynamic point contacts. Mech. Mach. Theory. 124, 179–196 (2018)

    Article  Google Scholar 

  13. M. Minervini, M.E. Mognaschi, P. Di Barba, L. Frosini, Convolutional neural networks for automated rolling bearing diagnostics in induction motors based on electromagnetic signals. Appl. Sci. 11(17), 7878 (2021)

    Article  CAS  Google Scholar 

  14. Lu. Jiantao, W. Qian, S. Li, R. Cui, Enhanced K-nearest neighbor for intelligent fault diagnosis of rotating machinery. Appl. Sci. 11, 919 (2021)

    Article  Google Scholar 

  15. F. Piltan, J.-M. Kim, B.F.I.U.M. Learning, A.C.F. Observer, Appl. Sci. 10, 5827 (2020)

    Article  CAS  Google Scholar 

  16. J.J. Saucedo-Dorantes, I. Zamudio-Ramirez, J. Cureno-Osornio, R.A. Osornio-Rios, J.A. Antonino-Daviu, Condition monitoring method for the detection of fault graduality in outer race bearing based on vibration-current fusion, statistical features and neural network. Appl. Sci. 11, 8033 (2021)

    Article  CAS  Google Scholar 

  17. J. Castilla-Gutiérrez, J.C. Fortes, J.M. Davila, Control and prediction protocol for bearing failure through spectral power density. Eksploatacja i Niezawodnosc—Maintenance Reliab. 4, 651–657 (2020)

    Article  Google Scholar 

  18. Z. Liu, L. Zhang, A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings. Measurement. 149, 107002 (2020)

    Article  Google Scholar 

  19. Y.N. Aldeoes, P. Ghockle, S.Y. Sondkar, Comparison of Machine Learning Algorithms for Bearing Failures Classification and Prediction. Advances in Electrical and Computer Technologies. ICAECT 2021, vol. 881, pp. 269–282 (2022)

  20. NSK report Technical Report | Bearing Library | Services | NSK Global

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Correspondence to Ali Hemati.

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Hemati, A., Shooshtari, A. Bearing Failure Analysis Using Vibration Analysis and Natural Frequency Excitation. J Fail. Anal. and Preven. 23, 1431–1437 (2023). https://doi.org/10.1007/s11668-023-01700-0

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  • DOI: https://doi.org/10.1007/s11668-023-01700-0

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