Inferring Mechanical Fault Models from the Electrical Domain

Dall'Ora, Nicola; Tosoni, Francesco; Fraccaroli, Enrico; Fummi, Franco

doi:10.1109/icps51978.2022.9817009

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…The taxonomy shown in Table 2 is derived essentially from the simulation of several mechanical systems described through the electrical analogy: a mass-springdamper, a tuned mass-spring-damper, a double pendulum, and a DC motor [21], [27]. Thus, the presented fault taxonomy can be extended by building equivalent circuits of more complex mechanical systems and simulating their behavior.…”

Section: Parametric Faultsmentioning

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: Parametric Faultsmentioning

confidence: 99%