🤖 AI Summary
This study addresses the challenges of production disruptions and high maintenance costs caused by sudden failures in industrial hydraulic pumps by proposing an unsupervised early fault detection method that relies solely on normal operational data. The approach compares a feedforward autoencoder, which processes single-frame sensor snapshots, with an LSTM-based autoencoder that models short-term temporal windows. Both models are trained exclusively on 52-channel, minute-level sensor logs without any fault examples. Evaluated on an independent test set containing seven annotated fault intervals, both architectures achieve highly reliable detection performance. The results validate the effectiveness of temporal modeling for industrial anomaly detection and demonstrate the practical feasibility and real-world applicability of unsupervised methods in industrial settings.
📝 Abstract
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.