  Condition-Based Maintenance is pivotal in enabling the early detection of
potential failures in engineering systems, where precise prediction of the
Remaining Useful Life is essential for effective maintenance and operation.
However, a predominant focus in the field centers on predicting the Remaining
Useful Life using unprocessed or minimally processed data, frequently
neglecting the intricate dynamics inherent in the dataset. In this work we
introduce a novel methodology grounded in the analysis of statistical
similarity within latent data from system components. Leveraging a specifically
designed architecture based on a Vector Quantized Variational Autoencoder, we
create a sequence of discrete vectors which is used to estimate system-specific
priors. We infer the similarity between systems by evaluating the divergence of
these priors, offering a nuanced understanding of individual system behaviors.
The efficacy of our approach is demonstrated through experiments on the NASA
commercial modular aero-propulsion system simulation (C-MAPSS) dataset. Our
validation not only underscores the potential of our method in advancing the
study of latent statistical divergence but also demonstrates its superiority
over existing techniques.
