Maria del Carmen Rubiales Mena

Time Series-Based Anomaly Detection in Gas Turbines Using TimeWGAN-GP

Experimental findings demonstrate that the proposed framework captures dynamic transitions more effectively than threshold-based methods, predicts the progression of faults, and supports the construction of an expanded anomaly catalog by generating synthetic profiles that resemble documented failure modes.