  This paper describes a pattern recognition approach aiming to estimate fuel
cell duration time from electrochemical impedance spectroscopy measurements. It
consists in first extracting features from both real and imaginary parts of the
impedance spectrum. A parametric model is considered in the case of the real
part, whereas regression model with latent variables is used in the latter
case. Then, a linear regression model using different subsets of extracted
features is used fo r the estimation of fuel cell time duration. The
performances of the proposed approach are evaluated on experimental data set to
show its feasibility. This could lead to interesting perspectives for
predictive maintenance policy of fuel cell.
