A Deep Learning Approach for Unsupervised Failure Detection in Smart Industry (Discussion Paper)

We propose an unsupervised anomaly detection model that is able to identify abnormal behavior by analysing streaming data coming from IoT sensors installed on critical devices. The proposed model is based on a Siamese neural network which embeds time series windows in a latent space, thus generating distance-based clusters of normal behavior. We experiment the proposed model on a case study aimed at the predictive maintenance of elevators where specific sensors measure the oscillations of the lift during its daily use. The experiments show that the proposed model successfully isolates anomalous oscillations thus correlating them to prospective malfunctions and thus preventing possible faults.