Performance monitoring and wear comprehension through Neural Network

In this paper, we present a novel approach to modeling the wear of complex dynamic systems, exemplified by aircraft engines, through the construction of a structured latent space.Unlike traditional methods, our model does not rely on explicit wear data but instead leverages supervised training to minimize the error on observable system parameters.Beyond wear forecasting, this work offers a foundation for unsupervised diagnosis, risk prevention, and the quantification of repair impacts.