  Prognostics aid in the longevity of fielded systems or products. Quantifying
the system's current health enable prognosis to enhance the operator's
decision-making to preserve the system's health. Creating a prognosis for a
system can be difficult due to (a) unknown physical relationships and/or (b)
irregularities in data appearing well beyond the initiation of a problem.
Traditionally, three different modeling paradigms have been used to develop a
prognostics model: physics-based (PbM), data-driven (DDM), and hybrid modeling.
Recently, the hybrid modeling approach that combines the strength of both PbM
and DDM based approaches and alleviates their limitations is gaining traction
in the prognostics domain. In this paper, a novel hybrid modeling approach for
prognostics applications based on combining concepts from fuzzy logic and
generative adversarial networks (GANs) is outlined. The FuzzyGAN based method
embeds a physics-based model in the aggregation of the fuzzy implications. This
technique constrains the output of the learning method to a realistic solution.
Results on a bearing problem showcases the efficacy of adding a physics-based
aggregation in a fuzzy logic model to improve GAN's ability to model health and
give a more accurate system prognosis.
