用於剩餘使用壽命估計的時間序列基礎模型嵌入

摘要

剩餘使用壽命（RUL）預測對於工業預測性維護至關重要，然而許多基於學習的方法仍需依賴大量的特徵工程或大規模標註數據集來訓練任務專屬的序列模型。在本研究中，我們提出一種輕量級學習方法，利用凍結的預訓練時間序列基礎模型（TSFM），並結合一個小型回歸頭，以從多變量感測器數據流進行剩餘使用壽命估計。具體而言，我們採用Chronos-2作為凍結骨幹來提取上下文窗口特徵，並訓練一個輕量級回歸神經網路進行剩餘使用壽命預測。基於兩種設備類型之實際工業感測器數據的實驗結果顯示，在相同的預處理與評估協議下，Chronos-2特徵表現始終優於遞迴、卷積、基於Transformer以及梯度提升等基線模型。我們進一步分析上下文長度的影響，發現較長的歷史資料能顯著提升預測性能，這表明時間序列基礎模型表示法為工業環境中的剩餘使用壽命估計提供了實用且資料高效的替代方案。

English

Remaining Useful Life (RUL) prediction is essential for industrial predictive maintenance, yet many learning-based approaches rely on extensive feature engineering or large labeled datasets to train task-specific sequence models. In this work, we introduce a lightweight learning approach, in which we leverage a frozen pretrained time-series foundation model (TSFM) and combine it with a small regression head for RUL estimation from multivariate sensor streams. More specifically, we use Chronos-2 as a frozen backbone to extract context window features and train a lightweight regression neural network for RUL prediction. Experiments on real-world industrial sensor data from two device types show that Chronos-2 features consistently improve over recurrent, convolutional, Transformer-based, and gradient-boosting baselines under the same preprocessing and evaluation protocol. We further analyze the impact of context length and find that performance improves significantly with longer histories, indicating that TSFM representation offer a practical and data-efficient alternative for RUL estimation in industrial settings.