Quality Control

Dependability Modelling under Uncertainty: An Imprecise by Philipp Limbourg

By Philipp Limbourg

Mechatronic layout techniques became shorter and extra parallelized, triggered through turning out to be time-to-market strain. equipment that permit quantitative research in early layout levels are required, should still dependability analyses objective to persuade the layout. as a result constrained volume of knowledge during this part, the extent of uncertainty is excessive and specific modeling of those uncertainties turns into necessary.

This paintings introduces new uncertainty-preserving dependability equipment for early layout phases. those contain the propagation of uncertainty via dependability types, the activation of knowledge from related parts for analyses and the combination of doubtful dependability predictions into an optimization framework. it's proven that Dempster-Shafer idea may be an alternative choice to likelihood concept in early layout level dependability predictions. specialist estimates could be represented, enter uncertainty is propagated in the course of the process and prediction uncertainty could be measured and interpreted. The ensuing coherent method could be utilized to symbolize the uncertainty in dependability models.

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6) depends, is expressed by a variable ss . The basic premise of system dependability prediction is that ss can be obtained by knowing the current states of the components forming the component state vector cs and the system structure, described by the system structure function φs . 18 (System function). Let Cs be the set of all possible component state vectors and Ss the set of all system states. , cn )T to a system state ss . Depending on the domains of Ss and Cs , system dependability models may be classified into different categories.

5) and thus the service (the behavior perceived by the user, def. 6) depends, is expressed by a variable ss . The basic premise of system dependability prediction is that ss can be obtained by knowing the current states of the components forming the component state vector cs and the system structure, described by the system structure function φs . 18 (System function). Let Cs be the set of all possible component state vectors and Ss the set of all system states. , cn )T to a system state ss . Depending on the domains of Ss and Cs , system dependability models may be classified into different categories.

In a probabilistic framework, PDFs or CDFs can be considered as measures of uncertainty, in the DST framework, a mass distribution plays this role. Uncertainty modeling and Propagation The main part of the framework comprises of the uncertainty modeling and propagation phases. Depending on the measures of uncertainty and the quantities of interest chosen, uncertainties can be modeled in different ways. In a probabilistic representation, they may be represented by a joint PDF on x. The joint PDF is commonly simplified either by using independence assumptions or dependency models to break down the multivariate PDF to univariate distributions, which are often represented by parametric functions such as Gaussian or Weibull distributions.

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