Modeling Cyber-Physical Production Systems with SystemC-AMS

Fraccaroli, Enrico; Vinco, Sara

doi:10.1109/tc.2022.3226567

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…N OWADAYS, in every industrial field, the design of complex systems is evolving quickly, as an effect of the adoption of digital technologies, in the form of the current trend named Industry 4.0, i.e., the fourth industrial revolution. The new digital technologies allow indeed to closely monitor and mimic the evolution of a Cyber-Physical System (CPS), thus gaining more knowledge of the operating conditions, allowing to foresee future evolution and to observe complex interdependencies [1]. The modeling complexity is, on the other hand, rewarded by the possibilities that it opens, in that it allows to mirror the actual operation of the analog side of a CPS or a smart system.…”

Section: Introductionmentioning

confidence: 99%

Multidomain Fault Models Covering the Analog Side of a Smart or Cyber–Physical System

Tosoni,

Dall’Ora,

Fraccaroli

et al. 2024

IEEE Trans. Comput.

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Over the last decade, the industrial world has been involved in a massive revolution guided by the adoption of digital technologies. In this context, complex systems like cyber-physical systems play a fundamental role since they were designed and realized by composing heterogeneous components. The combined simulation of the behavioral models of these components allows to reproduce the nominal behavior of the real system. Similarly, a smart system is a device that integrates heterogeneous components but in a miniaturized form factor. The development of smart or cyber-physical systems, in combination with faulty behaviors modeled for the different physical domains composing the system, enables to support advanced functional safety assessment at the system level. A methodology to create and inject multi-domain fault models in the analog side of these systems has been proposed by exploiting the physical analogy between the electrical and mechanical domains to infer a new mechanical fault taxonomy. Thus, standard electrical fault models are injected into the electrical part, while the derived mechanical fault models are injected directly into the mechanical part. The entire flow has been applied to two case studies: a direct current motor connected with a gear train, and a three-axis accelerometer.

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Section: Introductionmentioning

confidence: 99%