prognostics

该论文提出 Survprompt 框架，将结构化患者协变量转为自由文本文本，用零样本提示预测 LLM 的生存时间，结果显示 GPT-5.6-Sol 的 cMAE 接近专门训练的随机生存森林（RSF），但在风险分层（c-index）及不同癌种、机构间的一致性上仍存在明显局限。

A systematic literature review of 212 studies investigates how Physics-Informed Machine Learning (PIML) is applied in Prognostics and Health Management (PHM), introducing a four-class classification scheme and finding that PIML consistently improves predictive performance over conventional baselines, though the literature is skewed toward batteries and bearings and lacks strong evidence for claims regarding generalization and interpretability.

This paper presents a controlled study of federated learning for aircraft-engine remaining-useful-life prediction under both benign and adversarial client heterogeneity, evaluating personalization and Byzantine-robust aggregation methods. It finds that shared-representation personalization closes much of the local-central accuracy gap, robust aggregation with Krum effectively mitigates backdoor attacks, and combining both yields a composed defense with low attack success at a modest accuracy cost.

This paper proposes a framework for applying tabular foundation models to industrial time series for prognostics and health management, demonstrating strong performance and data efficiency across multiple PHM tasks.

This paper presents a multi-task scientific machine learning framework for turbine prognostics that jointly predicts engine health metrics and remaining useful life with quantified uncertainty, using a shared sequence encoder and task-specific heads.

This paper benchmarks five uncertainty quantification methods for neural network predictions of turbine gas temperature, evaluating trade-offs in coverage, width, and stability to guide prognostics and health management in engines.