# LSTM VS. Feed-Forward Autoencoders for Unsupervised Fault Detection in Hydraulic Pumps

arXiv: https://arxiv.org/abs/2601.11163 · HTML: https://mlsanity.com/paper/2601.11163/

## Key facts

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- First posted on arXiv 16 Jan 2026.

## Abstract (arXiv)

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.