  Electrochemical Impedance Spectroscopy (EIS) is a crucial technique for
assessing corrosion of a metallic materials. The analysis of EIS hinges on the
selection of an appropriate equivalent circuit model (ECM) that accurately
characterizes the system under study. In this work, we systematically examined
the applicability of three commonly used ECMs across several typical material
degradation scenarios. By applying Bayesian Inference to simulated corrosion
EIS data, we assessed the suitability of these ECMs under different corrosion
conditions and identified regions where the EIS data lacks sufficient
information to statistically substantiate the ECM structure. Additionally, we
posit that the traditional approach to EIS analysis, which often requires
measurements to very low frequencies, might not be always necessary to
correctly model the appropriate ECM. Our study assesses the impact of omitting
data from low to medium-frequency ranges on inference results and reveals that
a significant portion of low-frequency measurements can be excluded without
substantially compromising the accuracy of extracting system parameters.
Further, we propose simple checks to the posterior distributions of the ECM
components and posterior predictions, which can be used to quantitatively
evaluate the suitability of a particular ECM and the minimum frequency required
to be measured. This framework points to a pathway for expediting EIS
acquisition by intelligently reducing low-frequency data collection and
permitting on-the-fly EIS measurements
