  Data-centric prognostics is beneficial to improve the reliability and safety
of proton exchange membrane fuel cell (PEMFC). For the prognostics of PEMFC
operating under dynamic load, the challenges come from extracting degradation
features, improving prediction accuracy, expanding the prognostics horizon, and
reducing computational cost. To address these issues, this work proposes a
data-driven PEMFC prognostics approach, in which Hilbert-Huang transform is
used to extract health indicator in dynamic operating conditions and
symbolic-based gated recurrent unit model is used to enhance the accuracy of
life prediction. Comparing with other state-of-the-art methods, the proposed
data-driven prognostics approach provides a competitive prognostics horizon
with lower computational cost. The prognostics performance shows consistency
and generalizability under different failure threshold settings.
