  This paper addresses the use of data-driven evolving techniques applied to
fault prognostics. In such problems, accurate predictions of multiple steps
ahead are essential for the Remaining Useful Life (RUL) estimation of a given
asset. The fault prognostics' solutions must be able to model the typical
nonlinear behavior of the degradation processes of these assets, and be
adaptable to each unit's particularities. In this context, the Evolving Fuzzy
Systems (EFSs) are models capable of representing such behaviors, in addition
of being able to deal with non-stationary behavior, also present in these
problems. Moreover, a methodology to recursively track the model's estimation
error is presented as a way to quantify uncertainties that are propagated in
the long-term predictions. The well-established NASA's Li-ion batteries data
set is used to evaluate the models. The experiments indicate that generic EFSs
can take advantage of both historical and stream data to estimate the RUL and
its uncertainty.
