The fourth industrial age takes the manufacturing factory to a new level by introducing smart, extendible, flexible, modular and customized mass production technologies. Production lines or machines need to be integrated at the management level to be industry 4.0 compliant: in this way they can create and optimize a customer-oriented production, while constantly maintaining good performance conditions. In this context, one of the main challenges is the possibility to detect faults as fast as possible, to accurately diagnose those faults which can negatively affect the overall production cycle, and finally address them before it is too late. Due to the great importance that electric motors play in this context, an online smart algorithm for fault detection in electric motors is proposed in this paper. The effectiveness of the proposed method has been validated by applying it on an experimental benchmark, where the results show that the method is accurate and fast in detection of faults.
Online Fault Detection: A Smart Approach for Industry 4.0 / Prist, Mariorosario; Monteriù, Andrea; Freddi, Alessandro; Cicconi, Paolo; Giuggioloni, Federico; Caizer, Eduard; Verdini, Carlo; Longhi, Sauro. - ELETTRONICO. - (2020), pp. 167-171. (Intervento presentato al convegno 2020 IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2020 tenutosi a ita nel 2020) [10.1109/MetroInd4.0IoT48571.2020.9138295].
Online Fault Detection: A Smart Approach for Industry 4.0
Prist Mariorosario;Monteriù Andrea;Freddi Alessandro;Cicconi Paolo;Longhi Sauro
2020-01-01
Abstract
The fourth industrial age takes the manufacturing factory to a new level by introducing smart, extendible, flexible, modular and customized mass production technologies. Production lines or machines need to be integrated at the management level to be industry 4.0 compliant: in this way they can create and optimize a customer-oriented production, while constantly maintaining good performance conditions. In this context, one of the main challenges is the possibility to detect faults as fast as possible, to accurately diagnose those faults which can negatively affect the overall production cycle, and finally address them before it is too late. Due to the great importance that electric motors play in this context, an online smart algorithm for fault detection in electric motors is proposed in this paper. The effectiveness of the proposed method has been validated by applying it on an experimental benchmark, where the results show that the method is accurate and fast in detection of faults.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
