In the realm of predictive maintenance, the incorporation of artificial intelligence (AI) methods has revolutionized the field by empowering businesses to actively monitor and preemptively address equipment malfunctions. Detecting anomalies plays a crucial role in predictive maintenance as it serves as an early indicator of potential faults or failures. This paper introduces initial findings from the use of autoencoders and their associated vector reconstruction error within the context of the IMOCO4.E project.
Detection of Component Degradation: A Study on Autoencoder-Based Approaches / Guidotti, D.; Pandolfo, L.; Pulina, L.. - (2023). (Intervento presentato al convegno 19th IEEE International Conference on e-Science, e-Science 2023 tenutosi a cyp nel 2023) [10.1109/e-Science58273.2023.10254890].
Detection of Component Degradation: A Study on Autoencoder-Based Approaches
Guidotti D.;Pandolfo L.;Pulina L.
2023-01-01
Abstract
In the realm of predictive maintenance, the incorporation of artificial intelligence (AI) methods has revolutionized the field by empowering businesses to actively monitor and preemptively address equipment malfunctions. Detecting anomalies plays a crucial role in predictive maintenance as it serves as an early indicator of potential faults or failures. This paper introduces initial findings from the use of autoencoders and their associated vector reconstruction error within the context of the IMOCO4.E project.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
