ScholarWorks@경상국립대학교

초록

Solid oxide fuel cell (SOFC) stacks face reliability challenges because multiple degradation mechanisms interact with operational and environmental variability. We develop a hierarchical Bayesian framework that couples a monotone area-specific resistance (ASR) growth law with a Weibull time-to-failure model and employs a Student-t observation layer to down-weight outliers. Using multi-cell data, the approach narrows to 95 % predictive-interval widths for ASR and lifetime by up to 33 % relative to a non-hierarchical baseline, and global sensitivity analysis identifies the ASR growth rate as the dominant driver (S1 ≈ 0.84). Scenario projections quantify operational effects: hot–humid climates raise failure probability to ≈56 % versus ≈46 % under cold–dry conditions, whereas moderate load variations are negligible within normal ranges. External validation on a ∼93 000 h record shows low root-mean-square and means absolute errors with near-nominal predictive-interval coverage. Collectively, these results establish a diagnostic-to-decision workflow for reliability modeling that improves confidence in lifetime predictions and supports data-informed operation and maintenance.

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