Advancing industrial gas turbine field performance testing: a review of procedures and key considerations with emerging technologies

This review explores the possibility of enhancing the efficiency and accuracy of Industrial Gas turbine Performance testing by critically assessing the traditional methods, their limitations, and how modern technologies can be used to complement the existing traditional testing approaches, optimize data acquisition, and predict operational failures. A systematic and comprehensive search strategy was employed to identify relevant academic and industry literature. Studies on traditional testing practices were reviewed to highlight their constraints, while researches involving the application of emerging technologies for performance diagnostics were also reviewed to illustrate their benefits. Findings show that measured data such as turbine inlet temperature, compressor pressure ratio, exhaust temperature, fuel flow, shaft speed, and vibration remain essential for both traditional and AI-enhanced methods. These parameters, typically obtained through standardized testing procedures, provide the foundational input for AI models such as machine learning algorithms and digital twins. The study revealed that AI technologies thrive in data-rich, repeatable environments by enhancing processes like instrumentation, data logging, and normalization. The study also revealed that machine learning, deep learning, artificial neural networks, and digital twins can be used for more effective planning, reduce redundant testing, and mitigate delays caused by variable factors like weather or load conditions.