Machine learning-based diagnosis and health monitoring of solid oxide fuel cells using electrochemical impedance spectroscopy under multiple failure modes

Accurate identification of failure modes is essential for predictive maintenance of hydrogen-fueled solid oxide fuel cells (SOFCs). This study presents an electrochemical impedance-based failure mode diagnosis framework trained on experimentally generated degradation trajectories from twelve SOFCs subjected to four high-impact failure conditions: redox cycling, fuel starvation, short circuit, and thermal shock. Frequent electrochemical impedance spectroscopy measurements are converted into diagnostic signatures using distribution of relaxation times (DRT) analysis, and the resulting DRT-peak features and ohmic resistance are organized into labeled multivariate time-series windows for classification. The extracted DRT features are linked to underlying electrochemical phenomena within the cell, enabling failure modes to be interpreted through their impedance signatures rather than only through global power degradation. To benchmark diagnostic capability across different modeling paradigms, six classifiers are evaluated, including feature-based Random Forest (RF), XGBoost (XGB), and RF+XGB ensemble models, as well as sequence learners including Long Short-Term Memory, Temporal Convolutional Network, and an adapted attention-based Informer. Results demonstrate that temporal deep models provide improved balanced performance, with Informer achieving the strongest overall performance (Macro-F1 = 0.98). A sensitivity analysis further evaluates the repeatability of the impedance-derived features across repeated experiments for each failure mode. Finally, a computational cost analysis quantifies the performance-efficiency trade-off across model families.