用无监督自编码器检测液压泵故障，仅用正常数据就能精准识别异常。
LSTM VS. Feed-Forward Autoencoders for Unsupervised Fault Detection in Hydraulic Pumps
- 对比前馈与LSTM自编码器，捕捉单点与短时序数据特征。
- 仅用健康数据训练，仍能准确识别出7个标注故障区间。
- 适合工业设备早期故障预警，无需故障样本即可部署。
工业液压泵的意外故障会中断生产并造成巨大损失。本文探索两种无监督自编码器（AE）方案用于早期故障检测：一种是分析单个传感器快照的前馈模型，另一种是捕捉短时序窗口的长短期记忆（LSTM）模型。两个网络均仅在来自52个传感器通道的分钟级日志中健康的样本上进行训练；评估使用包含7个标注故障时段的独立数据集。尽管训练阶段未包含故障样本，模型仍表现出高可靠性。
原文摘要 · Abstract (English)
Unplanned failures in industrial hydraulic pumps can halt production and incur substantial costs. We explore two unsupervised autoencoder (AE) schemes for early fault detection: a feed-forward model that analyses individual sensor snapshots and a Long Short-Term Memory (LSTM) model that captures short temporal windows. Both networks are trained only on healthy data drawn from a minute-level log of 52 sensor channels; evaluation uses a separate set that contains seven annotated fault intervals. Despite the absence of fault samples during training, the models achieve high reliability.
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