Abstract
Within today's fiercely competitive industrial landscape, effective condition monitoring, diagnostics, and prognostics play pivotal roles. The digitization of equipment has exponentially expanded data availability across industrial processes, driving the development of advanced techniques that significantly enhance machine performance. This paper delves into a decade- spanning survey of the evolution of condition monitoring, diagnostics, and prognostics, specifically focusing on machine learning (ML)-based approaches for optimizing gas turbine operational efficiency. Through an exhaustive literature review, this publication evaluates the performance of ML models and their applications in the realm of gas turbines. It also addresses key challenges and opportunities in gas turbine research. Ultimately, the synthesis of data collected from various sources coupled with ML techniques demonstrates promising potential in enhancing the accuracy, robustness, precision, and overall performance of industrial gas turbine systems.
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