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This work investigates single-sensor localization of acoustic emissions (AEs) along a wind turbine blade with supervised neural networks. We evaluate six architectures: a Dense MLP, a 1D CNN on time-domain inputs, two 2D CNNs on linear spectrograms (LinSpec) and mel-spectrograms (MelSpec), a CRNN (2D CNN + LSTM), and RepVGG (Mini RepVGG in simulation, RepVGG-A0 in experiments). Three experimental
