The optimisation of diagnosis and maintenance processes is a fundamental task to guarantee the continuity, safety and efficiency of production activities in Smart Factory and the electrical sector in general. Smart Energy Monitoring thanks to advanced measurement technologies and the digital evolution in the field of energy is opening new perspectives on this task. This study explores Load Profiling as a tool for identifying faults and anomalies with an unsupervised approach, using “electrical signature quality” as a key parameter for optimisation. Specifically, with reference to an asynchronous motor as an experimental case study, the paper analyses the capability of Load Profiling and the effects of electrical signature quality on detection performance considering two different fault types.

The Impact of Energy Measurement in Fault Detection: A Preliminary Analysis of Load Profiling Capability

Tari, L.
;
Betta, G.;Ferrigno, L.;
2024-01-01

Abstract

The optimisation of diagnosis and maintenance processes is a fundamental task to guarantee the continuity, safety and efficiency of production activities in Smart Factory and the electrical sector in general. Smart Energy Monitoring thanks to advanced measurement technologies and the digital evolution in the field of energy is opening new perspectives on this task. This study explores Load Profiling as a tool for identifying faults and anomalies with an unsupervised approach, using “electrical signature quality” as a key parameter for optimisation. Specifically, with reference to an asynchronous motor as an experimental case study, the paper analyses the capability of Load Profiling and the effects of electrical signature quality on detection performance considering two different fault types.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11580/111904
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