Electrical Signature Analysis (ESA) is a powerful tool that uses the voltage and current signals of a machine to infer its health status. ESA can serve as a predictive maintenance tool for detecting common faults at an early stage, thus preventing expensive catastrophic failures and production outages, and extending equipment lifetime. In this study, a novel application of ESA and Machine Learning (ML) for working condition monitoring and health status assessment of a CNC mill is presented. Experimental results show the effectiveness of the proposed approach.
Anomaly detection using electrical signature analysis and machine learning:application to a CNC mill / Cocca, P.; Gökan, M.; Pesenti, V.; Stefana, E.; Bortolani, R.; Romagnoli, D.. - 58:8(2024), pp. 139-144. (Intervento presentato al convegno 6th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies, AMEST 2024 tenutosi a Cagliari, Italy) [10.1016/j.ifacol.2024.08.063].
Anomaly detection using electrical signature analysis and machine learning:application to a CNC mill
Stefana, E.;
2024
Abstract
Electrical Signature Analysis (ESA) is a powerful tool that uses the voltage and current signals of a machine to infer its health status. ESA can serve as a predictive maintenance tool for detecting common faults at an early stage, thus preventing expensive catastrophic failures and production outages, and extending equipment lifetime. In this study, a novel application of ESA and Machine Learning (ML) for working condition monitoring and health status assessment of a CNC mill is presented. Experimental results show the effectiveness of the proposed approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
