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Optimization of Maintenance and Repair Planning for Gas Turbines in the Oil and Gas Industry

Falamarzi, Amir Hossein | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 58302 (51)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Rafiee, Majid; Mostafavi, Mostafa
  7. Abstract:
  8. This research responds to the problem of how to optimize maintenance and repair planning for oil and gas industry gas turbines. With three questions driving the study—how to optimize maintenance planning, what are the drivers of its success, and what methodologies are currently available to be adopted—the research critically discusses existing methods and analytical procedures. The Nourabad case study in the Fars Province supports the empirical aspect through application of IPCMMS software data and historical performance records in five top-performing turbine subsystems. The core methodology integrates condition-based maintenance (CBM) concepts with a modified failure rate model from Rykov et al. (2022). CBM facilitates real-time condition-based interventions to limit unnecessary preventive maintenance, and the modified failure rate model compensates for asymmetrical workload redistribution upon component failures. An integrated objective function is developed to minimize total cost—factors including replacement of components, anticipated failure repairs between inspections, and system downtime—while considering diagnostic uncertainty by predicting component states at each inspection. Findings demonstrate that this hybrid strategy achieves significant cost benefits and downtime savings over conventional age-based and scheduled maintenance policies. Failure-pattern analysis and machine-learning-based predictions enhance scheduling precision. Load-transfer modeling accounts for the effects of parallel-system interdependencies and guides preemptive action. Probabilistic and hazard-function-based models offer rational decision-making in situations of doubt, creating an economically sound maintenance schedule. Through the integration of CBM, sophisticated failure-rate modeling, and uncertainty-conscious cost minimization, this research develops enough data, scalable template for optimizing maintenance in gas turbines operating in challenging industrial environments. Future research should develop real-time diagnostics further and extend the method to heterogeneous oil and gas facilities
  9. Keywords:
  10. Gas Turbines ; Preventive Maintenance ; Reduce Organizational Cost ; Energy Consumption Reduction ; Maintenance Scheduling

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