摘要：Proton exchange membrane fuel cells (PEMFCs) offer high efficiency and clean emissions for sustainable energy systems, yet accurate prediction of their Remaining Useful Life (RUL) remains challenging due to complex degradation mechanisms under dynamic operating conditions. This study introduces a temporal-causal inference framework that synergizes Graph Total Variation (GTV) regularization with Monte Carlo (MC) dropout to resolve the efficiency-accuracy trade-off in PEMFC prognostics. The approach dynamically infers degradation causality via Peter-Clark momentary conditional independence algorithms to construct time-lagged interaction graphs, while the GTV convolutional layer suppresses spurious correlations through spectral-domain regularization. By integrating MC dropout without Bayesian computational overhead, the framework enables computationally efficient probabilistic RUL estimation. Validation on the PHM 2014 dataset demonstrates state-of-the-art performance, achieving a 0.738 alpha-Coverage at 95% confidence intervals for probabilistic reliability, reducing RMSE compared to graph convolution baselines for accuracy superiority, and enhancing robustness verified in ablation studies, collectively advancing reliable fuel cell deployment in practice.

关键字：RUL estimation; Graph convolutional network; Proton exchange membrane fuel cells; Temporal causal inference

ISSN号：0951-8320

卷、期、页：卷265子辑B

发表日期：2026-01-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：一区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：RELIABILITY ENGINEERING ＆ SYSTEM SAFETY

参与作者：恩里克齐奥,张丽

通讯作者：何宇轩,乔凌云,彭世亮

第一作者：苏怀,杨兆铭,张劲军

论文类型：期刊论文

论文概要：何宇轩,乔凌云,恩里克齐奥,苏怀,张丽,杨兆铭,彭世亮,张劲军，A framework based on temporal causal inference graph neural networks for the probabilistic estimation of the remaining useful life of proton exchange membrane fuel cells，RELIABILITY ENGINEERING ＆ SYSTEM SAFETY，2026，卷265子辑B

论文题目：A framework based on temporal causal inference graph neural networks for the probabilistic estimation of the remaining useful life of proton exchange membrane fuel cells