整合基于生存的衰老模型与数据驱动的剩余使用寿命预测
Integrating Survival-Based Aging Models with Data-Driven RUL Prognostics
- Traton AB
- Halmstad University（哈尔姆斯塔德大学）
- RISE Research Institutes of Sweden AB（瑞典RISE研究院）
- KTH Royal Institute of Technology（瑞典皇家理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出一种概率融合框架，整合基于磨损的生存模型与基于传感器的深度学习模型，通过失效概率分布实现不确定性感知的自适应加权，在N-CMAPSS数据集上提升了剩余使用寿命预测的精度与校准性。
AI中文摘要：
预测性维护需要可靠的剩余使用寿命（RUL）估计。现有方法主要遵循两种范式：基于磨损的衰老模型，捕捉累积退化；以及基于传感器的数据模型，反映瞬时健康状况，每种方法仅提供部分信息。在本工作中，我们提出了一种概率融合框架，通过失效概率分布整合基于磨损和基于传感器的预测组件。基于连接磨损、潜在健康状态、传感器观测和失效的显式结构假设，我们推导出一个原则性的组合规则，该规则能够实现具有组件自适应权重的、考虑不确定性的集成。在实验上，我们通过使用参数化生存模型学习基于磨损的组件，并使用带有事后不确定性模型的一维卷积神经网络（1D-CNN）学习基于传感器的组件来评估这一组合规则。在多个N-CMAPSS数据集上的评估表明，与任一单独组件相比，融合模型提高了点精度，保持了C指数，并产生了更窄且校准良好的预测区间。结果强调了基于磨损的生存模型和基于传感器的深度学习模型的互补作用，并表明它们的概率整合为在生命周期内实现更稳健和一致的预测提供了一条结构化途径。
英文摘要：
Predictive maintenance requires reliable remaining useful life (RUL) estimation. Existing methods mainly follow two paradigms: wear-based aging models that capture cumulative degradation and sensor-driven data models that reflect instantaneous health conditions, each providing only partial information. In this work, we propose a probabilistic fusion framework that integrates wear-based and sensor-based prognostic components through failure probability distributions. Based on explicit structural assumptions linking wear, latent health, sensor observations, and failure, we derive a principled combination rule that enables uncertainty-aware integration with adaptive weighting of the components. Experimentally, we assess this combination rule by learning the wear-based component using a parametric survival model and the sensor-based component using a 1D convolutional neural network (1D-CNN) with a post-hoc uncertainty model. Evaluation on multiple N-CMAPSS datasets demonstrates that the fused model improves point accuracy, preserves the C-index, and produces narrower yet well-calibrated prediction intervals compared to either component alone. The results highlight the complementary roles of wear-based survival model and sensor-based deep learning model, and show that their probabilistic integration provides a structured pathway toward more robust and consistent prognostics over the life-time.
