Hydrogen-fueled gas turbines represent a promising technology due to their efficiency and environmental friendliness. However, these turbines operate under extreme conditions, making components prone to corrosion and subsequent hydrogen leakages. Addressing these leakages quickly and accurately is vital to ensure safety. This research introduces a pioneering approach using deep learning to estimate the location and
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