Bearing Failure Analysis and Reliability Improvement of Centrifugal Pumps in Nigeria’s Oil and Gas Processing Facilities

Authors

Ajogu Nwonu Orji

Dangote Fertilizer Limited, Lagos (Nigeria)

Victor Iyanoye

Dangote Fertilizer Limited, Lagos (Nigeria)

Ariyo Wole John

Dangote Fertilizer Limited, Lagos (Nigeria)

Akingbade Babatunde Richard

Dangote Fertilizer Limited, Lagos (Nigeria)

Adejumo Lukman Sola

Dangote Fertilizer Limited, Lagos (Nigeria)

Article Information

DOI: 10.47772/IJRISS.2026.10200181

Subject Category: Petroleum

Volume/Issue: 10/2 | Page No: 2406-2420

Publication Timeline

Submitted: 2026-02-13

Accepted: 2026-02-16

Published: 2026-02-28

Abstract

Bearing-related issues continue to be a significant cause of downtime for centrifugal pumps in oil and gas processing plants, especially in developing countries where harsh operating conditions, aging equipment, and mainly reactive maintenance strategies prevail. This research provides a thorough analysis of bearing failures and reliability evaluation for centrifugal pumps in selected oil and gas facilities in Nigeria. A mixed-method strategy was employed, which combined the analysis of historical maintenance data, physical examination of failed bearings, Failure Mode and Effects Analysis (FMEA), Root Cause Analysis (RCA), and reliability modeling through Weibull distribution and Mean Time Between Failures (MTBF). The findings suggest that the main causes of unplanned pump failures are lubrication degradation, contamination ingress, and shaft misalignment, which together make up many of these outages. Reliability analysis shows that the mean time between failures (MTBF) is considerably less than what the manufacturer anticipates, while Weibull shape parameters reveal a prevalence of early-life and random failure patterns. Considering these results, a practical framework for improving reliability is proposed, which includes condition-based monitoring, structured lubrication management, better installation practices, and reliability-centered maintenance (RCM). This study offers component-level insights tailored to specific contexts to improve the reliability and availability of centrifugal pumps in Nigerian and similar oil and gas processing settings.

Keywords

Centrifugal pumps; Bearing failure; Reliability analysis; Oil and gas; Nigeria; Maintenance engineering

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References

1. H. P. Bloch and F. K. Geitner, Machinery Failure Analysis and Troubleshooting. Oxford, UK: Elsevier, 2019. [Google Scholar] [Crossref]

2. R. K. Mobley, An Introduction to Predictive Maintenance, 3rd ed. Oxford, UK: Butterworth-Heinemann, 2020. [Google Scholar] [Crossref]

3. SKF Group, Rolling Bearing Damage and Failures. Gothenburg, Sweden, 2018. [Google Scholar] [Crossref]

4. ISO 15243, Rolling Bearings—Damage and Failures—Terms, Characteristics and Causes. Geneva, Switzerland: ISO, 2017. [Google Scholar] [Crossref]

5. 5 R. Budris, “Centrifugal pump bearings: Tips for improving reliability,” Water Technology Online, vol. 39, no. 6, pp. 1–6, 2014. [Google Scholar] [Crossref]

6. 6 M. Petrashko, “Proactive bearing maintenance,” Pumps & Systems, vol. 18, no. 5, pp. 34–39, 2011. [Google Scholar] [Crossref]

7. 7 T. Funmilayo and E. G. Saturday, “Cost-effective maintenance strategy for centrifugal pumps using RCM,” Int. J. Frontiers Eng. Technol. Res., vol. 5, no. 2, pp. 1–11, 2023. [Google Scholar] [Crossref]

8. 8 K. S. Jardine, D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics,” Mech. Syst. [Google Scholar] [Crossref]

9. J. Moubray, Reliability-Centered Maintenance. Oxford, UK: Butterworth-Heinemann, 1997. [Google Scholar] [Crossref]

10. R. K. Mobley and R. Keith, Maintenance Engineering Handbook, 6th ed. New York, NY: McGraw-Hill, 2002. [Google Scholar] [Crossref]

11. K. K. Raj, S. Kumar, and R. R. Kumar, “Systematic review of bearing component failure,” Arabian J. Sci. Eng., vol. 50, pp. 5353–5375, 2025. [Google Scholar] [Crossref]

12. P. Palit et al., “Rolling contact fatigue failure analysis,” J. Fail. Anal. Prev., vol. 25, pp. 62–69, 2025. [Google Scholar] [Crossref]

13. R. Li et al., “Failure analysis of a needle roller bearing,” J. Fail. Anal. Prev., vol. 24, pp. 108–115, 2024. [Google Scholar] [Crossref]

14. Z. Yang et al., “Failure analysis of aero-engine bearing,” Eng. Fail. Anal., vol. 150, 107298, 2023. [Google Scholar] [Crossref]

15. J. Hong et al., “Composite failure analysis,” Eng. Fail. Anal., vol. 165, 108707, 2024. [Google Scholar] [Crossref]

16. G. Lu et al., “Wear failure mechanisms in marine bearings,” Wear, vol. 530–531, 205047, 2023. [Google Scholar] [Crossref]

17. Z. Chang et al., “Failure mode of oil-air lubricated bearings,” Tribol. Int., vol. 112, pp. 68–74, 2017. [Google Scholar] [Crossref]

18. M. O. Jakobsen et al., “Vibration signatures in ball bearings,” Tribol. Int., vol. 156, 106840, 2021. [Google Scholar] [Crossref]

19. M. Sousa et al., “Oil-water emulsions,” J. Petrol. Sci. Eng., vol. 210, 110041, 2022. [Google Scholar] [Crossref]

20. L. Dai et al., “Reliability evaluation model of rolling bearings,” Reliab. Eng. Syst. Saf., vol. 225, 108646, 2022. [Google Scholar] [Crossref]

21. W. Kong and H. Li, “Remaining useful life prediction,” Appl. Soft Comput., vol. 129, 109630, 2022. [Google Scholar] [Crossref]

22. Y. Wang et al., “Remaining useful life prediction based on multi-feature fusion,” Measurement, vol. 201, 111572, 2022. [Google Scholar] [Crossref]

23. S. Schmidt et al., “Discrepancy analysis methodology,” Mech. Syst. Signal Process., vol. 116, pp. 40–61, 2019. [Google Scholar] [Crossref]

24. V. G. Salunkhe and R. G. Desavale, “Intelligent prediction for detecting bearing vibration,” J. Nondestruct. Eval., 2021. [Google Scholar] [Crossref]

25. V. G. Salunkhe et al., “Incipient fault detection for roller bearings,” J. Tribol., vol. 145, 2023. [Google Scholar] [Crossref]

26. V. G. Salunkhe et al., “Rolling element bearing fault diagnosis,” J. Tribol., vol. 147, 2025. [Google Scholar] [Crossref]

27. M. L. Mishra, M. Kumar, and M. L. Chandrawanshi, “Failure analysis of ball bearing in centrifugal pump,” Mater. Today: Proc., vol. 56, pp. 760–767, 2022. [Google Scholar] [Crossref]

28. J. Silva, P. Vaz, and P. M. Mom, “Reliability estimation using EM algorithm,” Appl. Sci., vol. 13, no. 13, 7736, 2023. [Google Scholar] [Crossref]

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