AI 中文总结
针对旋转机械剩余使用寿命预测，提出局部-全局特征混合器与趋势引导一致性损失，在公开轴承数据集上提升了长时预测稳定性与RUL预测性能，且可适配多种退化过程模型
AI 中文摘要
退化过程（DP）建模广泛应用于剩余使用寿命（RUL）预测，尤其在失效周期数据有限的场景中。神经网络可在不预设固定退化函数的情况下呈现复杂的退化轨迹，但递归健康指标（HI）预测易出现误差累积，且在长时程预测中难以保留不可逆的退化趋势。为解决这些局限，本研究提出局部-全局特征混合器（LGFM）与趋势引导展开一致性（TG-RC）损失。LGFM将原始HI序列与统计及退化相关特征结合，以降低对高频噪声的敏感性，同时捕捉局部变化与全局退化状态。TG-RC损失在传统单步均方误差基础上，补充了递归多步展开损失与基于全局趋势先验的软动态时间规整对齐项，从而缩小训练与递归推理的差异，同时引导预测轨迹朝向一致的退化方向。在两个公开轴承数据集上的实验表明，所提框架可提升不同检测时刻的长时HI外推稳定性与RUL预测性能；LGFM还保持了较低的计算复杂度，TG-RC损失可嵌入各类DP模型以改进其长时预测性能。
英文摘要
Degradation process (DP) modeling is widely used for remaining useful life (RUL) prediction, particularly when run-to-failure data are limited. Neural networks can present complex degradation trajectories without prescribing a fixed degradation function; however, recursive health indicator (HI) forecasting is prone to error accumulation and may fail to preserve the irreversible degradation trend over long horizons. To address these limitations, this study proposes a local-global feature mixer (LGFM) and trend-guided rollout-consistent (TG-RC) loss. The LGFM combines the original HI sequence with statistical and degradation-related features to reduce sensitivity to high-frequency noise and capture both local changes and global degradation states. The TG-RC loss supplements the conventional one-step mean squared error with a recursive multi-step rollout loss and a soft dynamic time warping alignment term based on a global trend prior. Consequently, it reduces the discrepancy between training and recursive inference, while guiding the predicted trajectory toward a consistent degradation direction. Experiments on two public bearing datasets show that the proposed framework improves long-term HI extrapolation stability and RUL prediction performance across different inspection times. The LGFM also maintains low computational complexity, while the TG-RC loss can be incorporated into various DP models to improve their long-term forecasting performance.
Comments37 pages, 12 figures
