用神经网络建模威布尔生存分析，提升维修系统寿命预测精度。
WTNN: Weibull-Tailored Neural Networks for survival analysis
- 基于威布尔分布设计神经网络架构，融合关键影响因子先验知识。
- 在代理指标与右删失数据上训练稳定，可生成可靠生存预测。
- 适合军事装备等高复杂度场景的寿命评估，结果可解释性强。
威布尔分布是描述受维护影响系统寿命的常用模型。当仅有代理指标和删失观测时，需将分布参数表示为时变协变量的函数。深度神经网络能灵活学习协变量与运行寿命间的复杂关系，扩展传统回归模型能力。针对军事车辆在高度变化且严苛环境下的运维分析，以及现有方法的局限性，本文提出WTNN——一种专为威布尔生存研究设计的神经网络建模框架。该架构通过结构化方式融入对最重要协变量的定性先验知识，符合威布尔分布的形状与结构。数值实验表明，该方法可在代理数据与右删失数据上可靠训练，生成稳健且可解释的生存预测，优于现有方法。
原文摘要 · Abstract (English)
The Weibull distribution is a commonly adopted choice for modeling the survival of systems subject to maintenance over time. When only proxy indicators and censored observations are available, it becomes necessary to express the distribution's parameters as functions of time-dependent covariates. Deep neural networks provide the flexibility needed to learn complex relationships between these covariates and operational lifetime, thereby extending the capabilities of traditional regression-based models. Motivated by the analysis of a fleet of military vehicles operating in highly variable and demanding environments, as well as by the limitations observed in existing methodologies, this paper introduces WTNN, a new neural network-based modeling framework specifically designed for Weibull survival studies. The proposed architecture is specifically designed to incorporate qualitative prior knowledge regarding the most influential covariates, in a manner consistent with the shape and structure of the Weibull distribution. Through numerical experiments, we show that this approach can be reliably trained on proxy and right-censored data, and is capable of producing robust and interpretable survival predictions that can improve existing approaches.
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