Effect of the Maintenance Strategy on the Performance and Efficiency of the Gas Turbine Unit: A Review

Abstract

With the intense competition characterizing the volatile power sector, the gas turbine industry is currently facing new challenges of increasing operational flexibility, reducing operating costs, and improving reliability and availability while mitigating environmental impact. In this complex and changing sector, the gas turbine community can meet a range of these challenges by developing highly accurate, computationally accurate and efficient diagnostic and warning systems to assess engine health. Recent studies have shown that monitoring engine gas path performance remains the cornerstone for making informed decisions in the operation and maintenance of gas turbines. Describes a newly developed engine performance monitoring methodology, diagnostic and forecasting techniques. The inception of performance monitoring and its evolution over time, the techniques used to generate a high-quality dataset using adaptive engine model performance, and the effects of computational intelligence techniques on enhancing the implementation of engine fault diagnosis are reviewed. Furthermore, recent developments in alarm technologies designed to enhance the maintenance decision-making scheme and the main causes of gas turbine performance degradation are discussed to facilitate the identification of unit faults. Gas turbine diagnostics and forecasts are one of the most important key technologies to enable the transition from scheduled maintenance to maintenance status in order to improve engine reliability and availability and reduce life cycle costs to organize, evaluate and identify patterns and trends in the literature as well as identify research gaps and recommend new research areas in the field of gas turbine performance-based monitoring.