Constructing a simulable model of a production line is crucial to ensure adequate maintenance, but it is nonetheless too complex due to the presence of highly heterogeneous components. In this perspective, Verilog-AMS is a promising solution, as it allows to cover different levels of details, from transistor-level and digital components to multi-physical dynamics. This paper shows how Verilog-AMS can be used to model production line components by exploiting multiple disciplines effectively. Furthermore, we will prove that Verilog-AMS allows efficient modeling of faults by inserting saboteurs and mutants in multi-physics descriptions. This methodology allows the definition of a multi-discipline fault injection technique that can be used to generate valuable data to support any analysis based on faulty temporal series, like predictive maintenance.

Multi-Discipline Fault Modeling with Verilog-AMS / Dall'Ora, Nicola; Fraccaroli, Enrico; Vinco, Sara; Fummi, Franco. - ELETTRONICO. - (2021), pp. 237-243. (Intervento presentato al convegno IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) tenutosi a Victoria, BC (Canada) nel 10-12 May 2021) [10.1109/ICPS49255.2021.9468133].

Multi-Discipline Fault Modeling with Verilog-AMS

Vinco, Sara;Fummi, Franco
2021

Abstract

Constructing a simulable model of a production line is crucial to ensure adequate maintenance, but it is nonetheless too complex due to the presence of highly heterogeneous components. In this perspective, Verilog-AMS is a promising solution, as it allows to cover different levels of details, from transistor-level and digital components to multi-physical dynamics. This paper shows how Verilog-AMS can be used to model production line components by exploiting multiple disciplines effectively. Furthermore, we will prove that Verilog-AMS allows efficient modeling of faults by inserting saboteurs and mutants in multi-physics descriptions. This methodology allows the definition of a multi-discipline fault injection technique that can be used to generate valuable data to support any analysis based on faulty temporal series, like predictive maintenance.
2021
978-1-7281-6207-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2989525