该框架解决了XAI方法缺乏不确定性量化的问题，做可解释AI或电力系统故障诊断的研究者可以直接参考其理论证明和实验设计。
该论文提出一个统一框架，将贝叶斯神经网络（BNN）后验分布通过Lipschitz连续归因算子映射为解释分布，并引入不确定性感知相关性归因算子（UA-RAO），利用均值、方差、变异系数、分位数和集合聚合度量来总结解释分布。理论部分提供了蒙特卡洛可达性和Wasserstein近似界限。在15类电能质量扰动分类基准上，深度集成与均值UA-RAO相比确定性基线提升了定位性能，其他UA-RAO摘要揭示了点估计归因中缺失的不确定性模式。该框架是领域无关的，可应用于任何BNN与Lipschitz连续归因算子的组合。
A Unified Framework for Uncertainty-Aware Explainable Artificial Intelligence: A Case Study in Power Quality Disturbance Classification
Post-hoc explainable AI (XAI) methods typically produce deterministic attribution maps, whereas Bayesian neural networks (BNNs) induce a distribution over explanations. Capturing the variability of this distribution is important for uncertainty-aware decision-making. This paper formalises the \emph{explanation distribution} as the push-forward measure of the BNN posterior through any Lipschitz-continuous attribution operator. It further proposes the uncertainty-aware relevance attribution operator (UA-RAO), a general family of operators that summarises the explanation distribution using the mean, variance, coefficient of variation, quantiles, and set-theoretic aggregation measures. Theoretical support is provided through Monte Carlo accessibility and Wasserstein approximation bounds. The framework is evaluated on a 15-class power quality disturbance (PQD) classification benchmark, comparing three BNN approximations paired with three attribution operators using relevance mass accuracy and intersection-over-union as localisation metrics. Results show that deep ensembles with the mean UA-RAO improve localisation over the deterministic baseline, while other UA-RAO summaries reveal uncertainty patterns absent from point-estimate attributions. Qualitative results on measured signals further suggest that these patterns generalise beyond the synthetic training distribution. The framework is domain-agnostic and can be applied to any BNN paired with a Lipschitz-continuous attribution operator.