发表机构
University of Hertfordshire（赫特福德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于双阶段MLP-QRNN的分位数引导特征提取框架，将预测性维护的特征提取视为依赖于预测时域的表示学习阶段，实验表明该方法在不同时域上显著优于固定预处理方法。
AI 中文摘要
在数据驱动的预测性维护（PdM）中，特征提取通常被视为固定的预处理步骤：选择一组描述符并重复使用，而下游模型或预测时域则发生变化。本文独立出表示学习阶段，提出了一种基于双阶段MLP-QRNN层级结构的分位数引导特征提取框架。QRNN1为每个传感器通道学习一个宽泛的十分位数条件分布，而带有跳跃连接的QRNN2将保留的中尾分位数集细化为紧凑的、通道分辨的、分布感知的特征。一个固定的十三流水线消融实验跨越1小时、70小时和30天的时域，涉及9个工业设施中的72台机器，每个时域内的下游时间分类器保持固定。将保留的中尾分位数集从两个增加到四个，在启用注意力机制的情况下，30分钟和60分钟的F1分数分别提高到75.92%和72.44%。结果还表明，除非特征容量、时间嵌入、激活策略和传感器广度随预测任务进行缩放，否则表示在其设计时域之外无法可靠迁移。未修改的短时域提取器在70小时时F1分数降至42.90%，而时域条件提取器在70小时时达到60.38%，在30天时达到79.97%。因此，该框架支持将PdM特征提取视为依赖于时域的表示阶段，而非固定的预处理。
英文摘要
In data-driven predictive maintenance (PdM), feature extraction is usually treated as fixed preprocessing: a descriptor set is chosen once and reused while the downstream model or forecasting horizon changes. This paper isolates the representation-learning stage and presents a quantile-led feature-extraction framework based on a dual-stage MLP-QRNN hierarchy. QRNN1 learns a broad ten-quantile conditional distribution for each sensor channel, while skip-connected QRNN2 refines a retained mid-tail quantile set into compact, channel-resolved, distribution-aware features. A fixed thirteen-pipeline ablation spans 1-hour, 70-hour, and 30-day regimes across 72 machines in 9 industrial facilities, with the downstream temporal classifier held fixed within each regime. Increasing the retained mid-tail set from two to four quantiles improves 30- and 60-minute F1-score, reaching 75.92% and 72.44% with attention enabled. The results also show that representations do not transfer reliably beyond their design horizon unless feature capacity, temporal embedding, activation strategy, and sensor breadth are scaled with the forecasting task. The unmodified short-horizon extractor falls to 42.90% F1 at 70 hours, whereas horizon-conditioned extractors reach 60.38% at 70 hours and 79.97% at 30 days. The framework therefore supports treating PdM feature extraction as a horizon-dependent representational stage rather than fixed preprocessing.
