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.
Vector Reconstruction Error for Anomaly Detection: Preliminary Results in the IMOCO4.E Project / Guidotti, D.; Masiero, R.; Pandolfo, L.; Pulina, L.. - 2023-:(2023). ( 28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023 rou 2023) [10.1109/ETFA54631.2023.10275396].
Vector Reconstruction Error for Anomaly Detection: Preliminary Results in the IMOCO4.E Project
Guidotti D.;Pandolfo L.;Pulina L.
2023-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
