Improving the maintenance practice of pumps in crude oil production plant using reliability analysis
Abstract
The study ‘Improving the Maintenance Practice of Pumps in Crude Oil Production Plant Using Reliability Analysis’ was investigated in details. The primary objective of this study was to analyze the reliability of centrifugal pumps using failure data, with a focus on identifying the optimal maintenance strategy for pumps used in the crude oil export unit at the SPDC plant. A centrifugal pump from this unit served as the case study. Key metrics such as operational time, Mean Time Between Failure (MTBF), downtime, failure rate, repair rate (ƞ), reliability, unreliability, availability, and unavailability were calculated using the Monte Carlo reliability analysis model. Data from the maintenance department of SPDC, spanning a five-year period, was collected and analyzed. The study identified the failure modes of the pumps, and the reliability and availability of individual components of the pump. The results showed an increasing failure rate in pump components over time. For instance, the reliability of key components such as the bearing decreased from 26.89% in the first year to 6.39% by the fifth year, while the shaft seal dropped from 19.36% to 6.03%, and the ring declined from 19.36% to 5.92% over the same period. The volute exhibited the highest reliability, starting at 37.56% in the first year. The study reveals that every pump component had a sharp decline in uptime, or operational time, between the first and fifth years of use. The study also showed that external leakage-process (ELP) medium has a higher occurrence rate as a mode of failure and finally the study using reliability analysis has revealed components of pumps that require regular monitoring for maintenance to reduce downtime and increase productivity in the company. The study suggests transitioning from time-based maintenance to condition-based maintenance for certain components, while enhancing existing practices. Predictive maintenance techniques such as vibration, temperature, and noise monitoring are recommended to better predict failures, allowing for timely shutdowns and switchovers to improve efficiency and pump uptime. The bearing, shaft, and ring components have been identified from this study as the most significant, sensitive, and need particular care if the pumps' reliability is to be increased. This research highlights the need for a reliability-centered maintenance strategy to enhance the performance of centrifugal pumps in the crude oil export unit.
References
[1] Kutucuoglu, K. Y., Hamali, J., Irani, Z., & Sharp, J. M. (2001). A framework for managing maintenance using performance measurement systems. International journal of operations & production management, 21(1/2), 173–195. https://www.emerald.com/insight/content/doi/10.1108/01443570110358521/full/html
[2] Salonen, A. (2011). Strategic maintenance development in manufacturing industry. Doctoral dissertation, Mälardalen University.
[3] Udo, A. A., Ihom, P. A., Odeh, E. U., Offiong, A., & Markson, I. E. (In Press). Improving the maintenance practice of pumps in crude oil production plant using reliability analysis. Journal of materials and manufacturing technology. https://jmmt.reapress.com/journal/article/view/25
[4] Eti, M. C., Ogaji, S. O. T., & Probert, S. D. (2004). Implementing total productive maintenance in Nigerian manufacturing industries. Applied energy, 79(4), 385–401. DOI:10.1016/j.apenergy.2004.01.007
[5] Singh Panesar, S., & Markeset, T. (2008). Development of a framework for industrial service innovation management and coordination. Journal of quality in maintenance engineering, 14(2), 177–193. https://www.emerald.com/insight/content/doi/10.1108/13552510810877674/full/html
[6] Zainal, F. Y., & Rahim, N. A. (2014). Implementation of iec 60870-5-101 protocol on condition monitoring of water pump system. 3rd iet international conference on clean energy and technology (CEAT) 2014 (pp. 1–5). IET. https://ieeexplore.ieee.org/abstract/document/7151679/
[7] Farrar, C. R., & Worden, K. (2007). An introduction to structural health monitoring. Philosophical transactions of the royal society a: mathematical, physical and engineering sciences, 365(1851), 303–315. https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2006.1928
[8] Parida, A., Kumar, U., Galar, D., & Stenström, C. (2015). Performance measurement and management for maintenance: a literature review. Journal of quality in maintenance engineering, 21(1), 2–33. https://www.emerald.com/insight/content/doi/10.1108/JQME-10-2013-0067/full/html
[9] Sandberg, U., Ylipää, T., Skoogh, A., Isacsson, M., Stieger, J., Wall, H., … Agardtsson, J. (2014). Working with forces promoting or hindering implementation of strategies for maintenance–experiences from swedish industry [presentation]. Swedish production symposium 2014. https://core.ac.uk/download/pdf/70609727.pdf
[10] Ylipää, T., Skoogh, A., Bokrantz, J., & Gopalakrishnan, M. (2017). Identification of maintenance improvement potential using OEE assessment. International journal of productivity and performance management, 66(1), 126–143. https://www.emerald.com/insight/content/doi/10.1108/IJPPM-01-2016-0028/full/html
[11] Tsarouhas, P. H., Varzakas, T. H., & Arvanitoyannis, I. S. (2009). Reliability and maintainability analysis of strudel production line with experimental data–a case study. Journal of food engineering, 91(2), 250–259. https://www.sciencedirect.com/science/article/abs/pii/S0260877408004421d
[12] Besnard, F., Patriksson, M., Strömberg, A.-B., Wojciechowski, A., & Bertling, L. (2009). An optimization framework for opportunistic maintenance of offshore wind power system. 2009 IEEE bucharest powertech (pp. 1–7). IEEE. https://ieeexplore.ieee.org/abstract/document/5281868/
[13] Garg, H., & Sharma, S. P. (2012). Stochastic behavior analysis of complex repairable industrial systems utilizing uncertain data. ISA transactions, 51(6), 752–762. https://www.sciencedirect.com/science/article/pii/S0019057812000912
[14] Barberá, L., Crespo, A., Viveros, P., & Stegmaier, R. (2012). Advanced model for maintenance management in a continuous improvement cycle: integration into the business strategy. International journal of system assurance engineering and management, 3, 47–63. https://link.springer.com/article/10.1007/s13198-012-0092-y
[15] Abid, M., Ayub, S., Wali, H., & Tariq, M. N. (2014). Reliability centered maintenance plan for the utility section of a fertilizer industry: a case study. International journal of science and advanced technology, 4(3), 9–16. https://www.researchgate.net/profile/Muhammad-Abid-
[16] Kamiel, B. P. (2015). Vibration-based multi-fault diagnosis for centrifugal pumps. Doctoral dissertation, Curtin University.
[17] Aggarwal, A. K., Kumar, S., Singh, V., & Garg, T. K. (2015). Markov modeling and reliability analysis of urea synthesis system of a fertilizer plant. Journal of industrial engineering international, 11, 1–14. https://link.springer.com/article/10.1007/s40092-014-0091-5
[18] Corvaro, F., Giacchetta, G., Marchetti, B., & Recanati, M. (2017). Reliability, availability, maintainability (RAM) study, on reciprocating compressors API 618. Petroleum, 3(2), 266–272. DOI:1016/j.petlm.2016.09.002
[19] Olabisi, S. A., Ukpaka, C. P., & Nkoi, B. (2020). Application of reliability techniques to evaluate maintainability of centrifugal pump used for petroleum product delivery. Journal of newviews in engineering and technology (JNET), 2(3). https://www.rsujnet.org/wp-content/uploads/2023/04/JNET2020302.pdf
[20] Gupta, N., Kumar, A., & Saini, M. (2021). Reliability and maintainability investigation of generator in steam turbine power plant using ramd analysis. Journal of physics: conference series (Vol. 1714, p. 12009). IOP Publishing. https://iopscience.iop.org/article/10.1088/1742-6596/1714/1/012009/meta
[21] Komal, Sharma, S. P., & Kumar, D. (2010). RAM analysis of repairable industrial systems utilizing uncertain data. Applied soft computing, 10(4), 1208–1221. DOI:10.1016/j.asoc.2009.12.019
[22] Pedregal, D. J., Garcı́a, F. P., & Schmid, F. (2004). RCM2 predictive maintenance of railway systems based on unobserved components models. Reliability engineering & system safety, 83(1), 103–110. DOI:10.1016/j.ress.2003.09.020
[23] Yang, Q., Zhang, N., & Hong, Y. (2013). Reliability analysis of repairable systems with dependent component failures under partially perfect repair. IEEE transactions on reliability, 62(2), 490–498. https://ieeexplore.ieee.org/abstract/document/6510493/
[24] Gómez de León Hijes, F. C., & Cartagena, J. J. R. (2006). Maintenance strategy based on a multicriterion classification of equipments. Reliability engineering & system safety, 91(4), 444–451. DOI:10.1016/j.ress.2005.03.001
[25] Wang, H., & Chen, P. (2007). Sequential condition diagnosis for centrifugal pump system using fuzzy neural network. Neural information processing-letters and reviews, 11(3), 41–50.
Metrics
