用分位数表示法预测30天内设备故障，准确率达82.4%。
Long Horizon Transformer Quantile Fault Prediction for Multi Site Industrial Predictive Maintenance
- 将机器行为分位数建模为324维状态向量，构建30天长时序文档
- 在30天预测中实现81.82%精确率、82.39%准确率，优于18个基线模型
- 适合同厂区多设备长期维护场景，不支持跨厂区泛化
长周期预测性维护需区分缓慢退化与正常运行波动，时间跨度以天计而非小时。本文评估显式条件分位数表示是否可作为该问题的有效分类接口。提出TQRNN30d框架，结合双阶段分位数回归神经网络（QRNN）特征提取器与多流时序融合分类器。每小时81通道机器行为映射为324维分位数状态表示，720个有序小时构成输入30天模型的序列文档。分类器通过门控残差处理、因果循环编码及元数据条件交叉注意力，融合分位数状态、动态协变量、通道级静态元数据和168小时隐历史流。基于持续一小时预测误差发散的有界不稳定信号，在最长时域提供辅助记忆调制。评估基于9个制造厂共72台设备的机器无关划分（训练/验证/测试：43/14/15）。在30天时，TQRNN30d达到79.97% F1、80.18%召回率、81.82%精确率、82.39%准确率及0.820 ROC-AUC。在7、14、30天固定阈值比较中均优于全部18个基线，14天时F1提升最显著。结果支持在已观测同质九厂集群中的泛化性能，但未验证跨站点、跨设备或跨行业泛化能力。
原文摘要 · Abstract (English)
Long-horizon predictive maintenance requires models to distinguish slowly evolving degradation from normal operating-regime variation over planning windows measured in days rather than hours. This paper evaluates whether an explicit conditional-quantile representation provides an informative classifier interface for this problem. The proposed TQRNN30d framework combines a dual-stage quantile regression neural network (QRNN) feature extractor with a multi-stream temporal fusion classifier. Each hourly word of 81-channel machine behaviour is mapped to a 324-dimensional quantile-state representation, and 720 ordered hourly words form the 30-day document supplied to the long-horizon model. The classifier fuses quantile states with dynamic covariates, channel-level static metadata, and a 168-hour latent-history stream using gated residual processing, causal recurrent encoding, and metadata-conditioned cross-modal attention. A bounded instability-aware signal derived from sustained one-word-ahead prediction-error divergence provides auxiliary memory modulation at the longest horizon. Evaluation uses a machine-disjoint 43/14/15 train/validation/test allocation across 72 machines in nine manufacturing facilities. At 30 days, TQRNN30d achieves 79.97% F1, 80.18% recall, 81.82% precision, 82.39% accuracy, and 0.820 ROC-AUC. It leads all 18 evaluated baselines at the 7-, 14-, and 30-day fixed-threshold comparisons, with the largest F1 advantage at 14 days. The results support held-out-machine performance within the observed homogeneous nine-facility fleet, but do not establish unseen-site, cross-equipment, or cross-sector generalisation.
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