考虑内在因果关系的相关退化数据建模用于可靠性分析与剩余使用寿命预测
Modeling dependent degradation data considering inherent causal relationships for reliability analysis and remaining useful life prediction
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中文总结 AI 辅助
针对现有相关退化模型忽略因果方向性的问题，提出因果驱动框架，结合维纳过程、因果发现与贝叶斯融合建模，在C-MAPSS数据集上验证可提升退化和RUL预测精度。
中文摘要 AI 辅助
在复杂系统中，对相关退化过程进行精确建模对于可靠的可靠性评估和剩余使用寿命（RUL）预测至关重要。现有的相关退化模型通常使用基于相关性的方法描述相关性，这些方法具有对称特征。然而，它们忽略了退化路径之间的内在因果方向性，当存在物理因果关系时，这可能导致有偏的可靠性评估和RUL预测。为解决这一问题，本文提出了一种因果驱动的框架来对相关退化数据进行建模。首先，基于维纳过程为每个性能指标建立包含多源不确定性的单变量退化模型。然后，采用稳定的Peter-Clark算法揭示退化过程之间的内在因果关系，并利用不确定性感知神经网络在考虑不确定性的情况下量化因果效应。接下来，在贝叶斯框架内整合单变量退化预测和因果预测，构建因果相关退化模型，并推导相应的损失函数用于模型训练。最后，通过蒙特卡洛模拟推导系统可靠性和RUL预测。所提出的方法在C-MAPSS数据集上进行了验证。结果表明，考虑退化过程之间的内在因果方向性有助于消除物理上不切实际的退化行为，从而比独立模型和基于相关性的相关退化模型产生更准确的退化和RUL预测。
英文摘要
Accurate modeling of dependent degradation processes is essential for credible reliability assessment and remaining useful life (RUL) prediction in complex systems. Existing dependent degradation models usually describe dependence using correlation-based methods with symmetric characteristics. However, they ignore inherent causal directionality between degradation paths, which may lead to biased reliability evaluation and RUL predictions when physical causality exists. To address this issue, this paper proposes a causality-driven framework for modeling dependent degradation data. Firstly, univariate degradation models with multi-source uncertainties are established for each performance indicator based on the Wiener process. Then, the stable Peter-Clark algorithm is employed to uncover inherent causal relationships between degradation processes, and an uncertainty-aware neural network is employed to quantify causal effects considering uncertainties. Next, univariate degradation predictions and causal predictions are integrated within a Bayesian framework to construct a causally dependent degradation model, and the corresponding loss function is derived for model training. Finally, system reliability and RUL predictions are derived via Monte Carlo simulation. The proposed methodology is validated on the C-MAPSS dataset. Results show that considering inherent causal directionality between degradation processes helps eliminate physically unrealistic degradation behaviors, yielding more accurate degradation and RUL predictions than independent and correlation-based dependent degradation models.
发表机构
- School of Reliability and Systems Engineering, Beihang University（可靠性与系统工程学院，北京航空航天大学）
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