Siamese Networks for Unsupervised Failure Detection in Smart Industry

In industrial production systems, detecting malfunctions or unexpected behavior in devices early is crucial to avoid critical situations for both production plants and workers. In this context, we propose an unsupervised anomaly detection model that analyzes streaming data from IoT sensors installed on critical devices to identify abnormal behavior. Our model is based on a Siamese neural network, which embeds time series windows into a latent space, generating distance-based clusters representing normal behavior. We evaluate our model in a real case study focused on the predictive maintenance of elevators, where sensors measure lift oscillations during daily use. Experiments demonstrate that the model successfully isolates anomalous oscillations, correlating them with potential malfunctions and preventing possible faults.