Fault Identification on a Bearing Component of a Rotating Machine Using Vibration Spectrum Analysis


  • KT Aminu Abubakar Tatari Ali Polytechnic Bauchi, Nigeria
  • B D Halilu Abubakar Tatari Ali Polytechnic Bauchi, Nigeria
  • D Y Dabs Abubakar Tatari Ali Polytechnic Bauchi, Nigeria
  • M A Sule Abubakar Tatari Ali Polytechnic Bauchi, Nigeria
  • S U Ibrahim Federal Polytechnic Kaltungo, Gombe, Nigeria




Vibration, Fault Identification, Bearings, Signal Spectrum, Condition Monitoring


This paper presents an analysis of the vibration behavior of a rotating machine based on the signal spectrum for detecting localized defects in rotating bearings. This approach can extract the characteristic frequencies related to the defect from the resonant frequency band of the vibration signal. The technique is demonstrated on a machine tool with ball bearings (bearing number “2” and bearing number “4”) under a simulated crack on the bearing cage. Experimental results show some preliminary evidence that the vibration spectrum technique can be used to locate and predict the failure of rotating machine rolling element bearings from vibration data.


Zhang, L., and Lang, Z. 2018. Wavelet Energy Transmissibility Function and its Application to Wind Turbine Bearing Condition Monitoring, IEEE Transactions on Sustainable Energy, Vol. 9 (4). Pp. 1833 - 1843

Harlisca, C., and Szabo, L. 2012. Bearing Faults Condition Monitoring – A Literature Survey, Journal of Computer Science and Control Systems, Vol. 5 (2). Pp. 19 – 22

Janjarasjitt, S., Ocak, H., and Loparo, K. A. 2008. Bearing Condition Diagnosis and Prognosis Using Applied Nonlinear Dynamical Analysis of Machine Vibrational Signal, Journal of Sound and Vibration, 317. Pp. 112 – 126

Ai, S., and Yuping Zhang, H. 2009. Condition Monitoring for Bearing Using Envelope Spectrum of EEMD, In proceedings of International Conference on Measuring Technology and Mechatronics Automation. Pp. 190 – 193

Kiral, Z., and Karagulle, H. 2003. Simulation and Analysis of Vibration Signals Generated by Rolling Element bearing with Defects, Tribology International, 36. Pp. 667 – 678

Holm-Hasen, T. B., and Gao, X. R. 2000. Vibration Analysis of a Sensor-Integrated Ball Bearing, Transactions of the ASME, Journal of Vibration and Acoustics, Vol. 122. Pp. 384 – 392

Kulkarni, S., and Bewoor, A. 2006. Vibration-Based Condition Assessment of Ball Bearing with Distributed Defects, Journal of Measurements in Engineering, Vol. 4 (2). Pp. 87 – 94

Liu, J., Wang, W., and Ma, F. 2011. Bearing System Health Condition Monitoring Using Wavelet Cross-Spectrum Analysis Technique, Journal of Vibration and Control, Vol. 0 (0). Pp. 1 – 11

Halme, J., and Anderson, P. 2009. Rolling Contact Fatigue and Wear Fundamentals for Rolling Bearing Diagnostics – State of the Art, Review Paper. Pp. 377 – 393

KiranKumar, M. V., Lokesha, M., Kumar, S., and Kumar, A. 2018. Review on Condition Monitoring of Bearings Using Vibration Analysis Techniques, IOP Conference Series: Materials Science and Engineering, 376. Pp. 1 - 6



How to Cite

KT Aminu, B D Halilu, D Y Dabs, M A Sule, & S U Ibrahim. (2021). Fault Identification on a Bearing Component of a Rotating Machine Using Vibration Spectrum Analysis. International Journal of Advances in Scientific Research and Engineering (IJASRE), ISSN:2454-8006, 7(9), 37-42. https://doi.org/10.31695/IJASRE.2021.34059