AI 中文总结
针对预测性维护多元时间序列数据的生存分析，提出SurvCF(t)框架，通过识别资产运行历史变化增加预测寿命，将解释设为约束优化问题，经多基准评估，弥合生存预测与维护差距，实现可解释决策型AI。
AI 中文摘要
预测性维护依赖于准确的剩余使用寿命估计，通常通过对多元时间序列数据进行生存分析来制定。虽然现代深度生存模型具有很强的预测性能，但其黑箱性质限制了它们在需要可操作见解的安全关键环境中的应用。在这项工作中，我们引入了SurvCF(t)，这是第一个为在时间序列数据上运行的生存模型生成反事实解释的框架。SurvCF(t)识别对资产运行历史的最小、合理且时间一致的变化，这些变化会增加其预测寿命，将解释构建为一个结合有效性、接近性、稀疏性和合理性的约束优化问题。我们在多个基准上评估了该方法，包括C-MAPSS、N-CMAPSS以及Scania Component_X数据集的实际案例研究，证明了其产生可操作和可解释干预措施的能力。我们的结果表明，SurvCF(t)弥合了生存预测与规定性维护之间的差距，为维护策略实现了可解释和面向决策的人工智能。
英文摘要
Predictive maintenance relies on accurate Remaining Useful Life estimation, often formulated using survival analysis over multivariate time-series data. While modern deep survival models achieve strong predictive performance, their black-box nature limits their use in safety-critical settings where actionable insight is required. In this work, we introduce \textit{SurvCF(t)}, the first framework for generating counterfactual explanations for survival models operating on time-series data. \textit{SurvCF(t)} identifies minimal, plausible, and temporally consistent changes to an asset's operational history that increase its predicted life time, framing explanation as a constrained optimization problem combining validity, proximity, sparsity, and plausibility. We evaluate the method on multiple benchmarks, including C-MAPSS, N-CMAPSS, and a real-world case study of the Scania Component\_X dataset, demonstrating its ability to produce actionable and interpretable interventions. Our results show that \textit{SurvCF(t)} bridges the gap between survival prediction and prescriptive maintenance, enabling explainable and decision-oriented AI for maintenance strategies.
Comments10 pages, 3 figures
