Reliability Assessment and Maintenance Optimization of Hydraulic Rotary Drilling Rigs Used in Geotechnical Projects

The research has revealed that failure behaviour at the subsystem level is strongly related to the reliability and maintenance performance of hydraulic rotary drilling rigs, and hydraulic and mechanical components are the most prevalent in the proportion of failures, downtime, and maintenance burden. The assessment of reliability based on the parameters of MTBF and the MTTR showed that hydraulic and electrical failures were more frequent as well as took a higher amount of time to repair, which decreased the availability of the system in comparison to other subsystems. FMEA also identified that hydraulic-related failures like seal leakage, hose burst and others were the most dangerous at the operation level, which is why special attention to reliability was needed in these directions. On the basis of these findings, the research indicated that Reliability-Centered Maintenance and condition-based strategies should be adopted in providing priority on vital components, optimization of inspection intervals and minimization of unplanned breakdowns. Comprehensively, the findings validated the idea that data-driven reliability testing has a potential to exponentially improve rig availability, operational security as well as cost-efficiency of geotechnical drilling processes.

[1]   S. Akhtar, S. M. Al-Otaibi, W. S. Algaraawi, N. M. Alsanabani, K. S. Al-Gahtani, and A. Fnais, “Assessment of the lifespan of a site drilling machine in Saudi Arabia and India using correspondence analysis,” Sustainability, vol. 17, no. 9, p. 3865, Apr. 2025, doi: 10.3390/su17093865   [2]    S.-H. Chen, “Operation and maintenance of hydraulic structures,” in Hydraulic Structures - Theory and Applications,   Intech           Open,           2020.           doi: 10.5772/intechopen.91949.   [3]     A. A. I. Elrayah, “Enhancing drilling operations: prioritizing wellbore integrity, formation preservation, and effective mud waste control (case study),” Journal of Engineering and Applied Science, vol. 71, no. 1, Apr. 2024, doi: 10.1186/s44147-024-00383-0   [4]  P. Flegner, J. Kačur, G. Wittenberger, M. Durdán, and M. Laciak, “Identification and Evaluation of Vibration Sources from Experiments on Laboratory Drilling Equipment,” Machines, vol. 13, no. 12, p. 1076, Nov. 2025, doi: 10.3390/machines13121076. [5]  M. E. Hossain and A. A. Al-Majed, Fundamentals of Sustainable Drilling Engineering. John Wiley & Sons 2015.   [6]  Kattar, M., “Use of machine learning to optimize drilling equipment performance and life cycle focus: predictive maintenance” M.S. thesis, Dept. Petroleum and Mining Engineering, Polytechnic University of Turin, Italy, 2022   [7]    A. Khan, Y. Li, M. Shoaib, U. Sajjad, and F. Rui, “Utilizing Machine Learning and Digital Twin Technology for Rock Parameter Estimation from Drilling Data,” Journal of Intelligent Construction, vol. 3, no. 2, pp. 1–23, Apr. 2025, doi: 10.26599/jic.2025.9180088.   [8]    M. Krechowicz, W. Gierulski, S. Loneragan, and H. Kruse, “Human and Equipment Risk Factors Evaluation in Horizontal Directional Drilling Technology Using Failure Mode and Effect Analysis” vol. 12, no. 2, pp. 45-56, Jun. 2021, doi: 10.24425/mper.2021.137677 [9]  P. F. Li, X. F. Wang, Z. Yang, Z. J. Zhang, F. Yang, H. P. Tang, B. B. Zhang, “Advancing drilling parameter reliability: A data-driven comparison of MWD and DPM for stratigraphic identification,” Journal of Rock Mechanics and Geotechnical Engineering, vol. 18, no. 5, pp. 4094–4107, Jul. 2025, doi: 10.1016/j.jrmge.2025.06.005.   [10]   S. Mukhongo, J. Wakiru, P. N. Muchiri, and S. N. Mburu, "Risk-based Maintenance Decision Support for Geothermal Drilling Rigs: A Case Study for KenGen Drilling Rigs," in Proceedings World Geothermal Congress 2020, Reykjavik, Iceland, Apr. 2020