Vector Reconstruction Error for Anomaly Detection: Preliminary Results in the IMOCO4.E Project

In recent years, the integration of artificial intelligence (AI) techniques has significantly transformed the field of predictive maintenance, enabling businesses to proactively monitor and address potential equipment failures before they occur. One critical aspect of predictive maintenance is the detection of anomalies, which can serve as early warning signs for impending faults or failures. In this paper we present some preliminary results obtained by leveraging autoencoders and the related vector reconstruction error in the scope of the IMOCO4.E Project.