Development of Maintenance Management Model to Improve Availability Performance for Electric Generation System: A Case Study at Jakaya Kikwete Cardiac Institute
Abstract
This study developed a comprehensive hybrid maintenance management model to enhance the availability performance of electric power systems at Jakaya Kikwete Cardiac Institute (JKCI), Tanzania's premier specialised cardiac care facility. Traditional maintenance approaches, including preventive and reactive strategies, have resulted in uncontrolled downtimes, increased operational costs, and compromised medical care delivery, creating significant risks to patient safety and operational efficiency. The research employed a sophisticated mixed-method approach combining qualitative and quantitative techniques using a comprehensive case study methodology with triangulated data sources. Primary data was systematically collected through structured interviews with technical personnel, comprehensive site inspections using standardised evaluation checklists, and extensive expert consultations with experienced maintenance engineers. Secondary data encompassed detailed maintenance logs, operational records spanning 2018-2023, equipment performance histories, and manufacturer technical specifications. Failure Mode and Effects Analysis (FMEA) was rigorously utilised to identify and systematically assess critical failure modes, with Risk Priority Number (RPN) ranking and Risk Importance Index (RII) analysis employed for comprehensive risk assessment and prioritisation. The detailed analysis revealed fourteen critical failure modes, with seven classified as extremely high-risk (RII > 0.98), including grid outages, generator control system malfunctions, and battery/inverter failures that pose immediate threats to system continuity. These high-risk failures formed the foundation for the predictive model, with grid outage preparedness (β = 0.061) and thermal management/overheating issues (β = 0.049) emerging as the two most statistically significant predictors—directly addressing the most critical vulnerabilities identified through FMEA. A sophisticated hybrid maintenance model combining Condition-Based Maintenance (CBM) and Predictive Maintenance (PdM) strategies was developed using advanced multiple regression analysis with comprehensive coefficient evaluation. The model demonstrated exceptional statistical validity with a correlation coefficient R = 0.99, coefficient of determination R² = 0.98, and F-statistic = 642.78 (p < 0.001), confirming robust predictive capabilities. Comprehensive twelve-month validation showed remarkable accuracy with only 0.2% variance between predicted (87.8%) and actual (88%) availability performance across diverse operational conditions. The validated model successfully improved system availability from baseline 91% to over 99.5%, achieved a substantial 15% reduction in maintenance costs, and enhanced mean time between failures by 20% through optimised maintenance scheduling. The hybrid model provides a scalable, evidence-based framework for critical healthcare infrastructure maintenance, demonstrating significant potential for widespread application in similar facilities across developing countries facing comparable infrastructure challenges
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References
Ahmadi, A., Gupta, S., Karim, R., & Kumar, U. (2010). Selection of maintenance strategy for aircraft systems using multi-criteria decision making methodologies. International Journal of Reliability, Quality and Safety Engineering, 17(3), 223-243. https://doi.org/10.1142/S0218539310003779
Aibinu, A. A., & Al-Lawati, A. M. (2010). Using PLS-SEM technique to model construction organizations' willingness to participate in e-bidding. Automation in Construction, 19(6), 714-724. https://doi.org/10.1016/j.autcon.2010.02.016
Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research (3rd ed.). SAGE Publications.
Field, A. (2013). Discovering statistics using IBM SPSS Statistics (4th ed.). SAGE Publications.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75-105. https://doi.org/10.2307/25148625
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483-1510. https://doi.org/10.1016/j.ymssp.2005.09.012
Mobley, R. K. (2002). An introduction to predictive maintenance (2nd ed.). Butterworth-Heinemann. https://doi.org/10.1016/B978-075067531-4/50006-3
Power Africa. (2023). Healthcare electrification and telecommunications alliance report. United States Agency for International Development (USAID). https://www.usaid.gov/powerafrica/health
Stamatis, D. H. (2003). Failure mode and effect analysis: FMEA from theory to execution (2nd ed.). ASQ Quality Press.
World Health Organization. (2022). Global report on health facility infrastructure. WHO Press. https://www.who.int/publications/i/item/9789240042728
Yamane, T. (1967). Statistics: An introductory analysis (2nd ed.). Harper and Row.
Copyright (c) 2025 Victor Kaduma, Pius Victor Chombo, PhD
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