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.

翻译：工业液压泵的意外故障可能导致生产停滞并产生巨额成本。本文探讨了两种用于早期故障检测的无监督自编码器方案：一种分析单次传感器快照的全连接模型，以及一种捕捉短时时间窗口的长短期记忆模型。两种网络仅使用从52个传感器通道分钟级日志中提取的健康数据进行训练；评估则采用包含七个标注故障时段的独立数据集。尽管训练过程中未使用任何故障样本，两种模型均实现了较高的检测可靠性。