Uncertainty Management in Multidisciplinary Design of Critical Safety Systems

Patelli, Edoardo; Álvarez, Diego A.; Broggi, Matteo; Angelis, Marco de

doi:10.2514/1.i010273

Cited by 38 publications

(45 citation statements)

References 34 publications

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“…To estimate the most probable parameter values for each single device from the experimental C − V curves, a genetic algorithm (GA) have been adopted as a global optimization strategy [4]. The population size to explore the parameter space (design or search space) is an important factor determining the efficiency of the procedure [5,6]: adding more samples requires more function evaluations and thus, more computational effort.…”

Section: Uncertainty Quantification Through Genetic Algorithmsmentioning

confidence: 99%

Assessment of Micromechanically-Induced Uncertainties in the Electromechanical Response of MEMS Devices

Mirzazadeh¹,

Mariani²

2016

Proceedings of the 3rd International Electronic Conference on Sensors and Applications, 15–30 November 2016; Available Online:

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Microelectromechanical systems (MEMS) have been already successfully commercialized for around 20 years. The design of novel MEMS sensors currently targets two important features: smaller dimensions and higher reliability. As the characteristic size of the mechanical components of the devices decreases, uncertainties in the mechanical and geometrical properties induced by the microfabrication process become more and more important. To address these issues, an on-chip testing device has been proposed to avoid any visual inspection for the read-out. The electromechanical responses of ten nominally identical specimens have been recorded, and experimental data have shown a significant scattering due to the presence of relevant uncertainty sources. To interpret the response of the device, an analytical reduced-order model of the whole device has been developed. A genetic algorithm has then been adopted to identify features of the mechanical and geometrical uncertainties in the batch of test structures.

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Section: Uncertainty Quantification Through Genetic Algorithmsmentioning

confidence: 99%

Assessment of Micromechanically-Induced Uncertainties in the Electromechanical Response of MEMS Devices

Mirzazadeh¹,

Mariani²

2016

Proceedings of the 3rd International Electronic Conference on Sensors and Applications, 15–30 November 2016; Available Online:

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“…Beer et al (2008Beer et al ( , 2013 addressed this problem by imprecise probabilities. While Patelli et al (2014) merged with advanced Monte Carlo simulation and further stochastic techniques and implemented into Open-Cossan software to deal with epistemic uncertainty.…”

Section: Survival Function With Epistemic Uncertaintymentioning

confidence: 99%

“…Sensitivity analysis has many manifestations in probabilistic risk analyses and there are many disparate approaches based on various measures of influence and response. Patelli et al (2014) pointed out that due to the obvious importance of sensitivity analysis, it is essential to identify and rank the parameters that contribute mostly to the variability of the output of the system.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Survival signature-based sensitivity analysis of systems with epistemic uncertainties

Geng

Patelli²,

Beer

2015

Safety and Reliability of Complex Engineered Systems

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The survival signature provides a basis for efficient reliability assessment of systems with more than one component type. Often a perfect probabilistic modelling of the system is not possible due to limited information, vagueness and imprecision. Hence generalized probabilistic methods need to be used. These methods allow to explicitly model the uncertainties without the need of unjustified hypotheses and approximation. In this paper, a novel and efficient sensitivity approach is presented. The proposed approach is based on survival signature, allowing to identify and rank components in a system. A numerical example is used to illustrate the above methods.

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“…However, the techniques proposed usually require sophisticated simulation techniques (Patelli 2016;Patelli et al 2017a). For example, to propagate uncertainty through a complex black box model computationally expensive Monte Carlo or optimisation methods are used (Patelli et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Analytic Probabilistic Safety Analysis under Severe Uncertainty

Sadeghi

Angelis

Patelli

2020

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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Exact analytic expressions are given to evaluate the reliability of systems consisting of components, connected in parallel or series, subject to imprecise failure distributions. We also proposed a simplified version of the first order reliability method to deal with imprecision. This development allows engineers to evaluate the reliability of systems without having to resort to optimisation techniques and/or Monte Carlo simulation. In addition, this framework does not need to assume a distribution for the epistemic uncertainty, which permits a robust analysis even with limited data. In this way, the approach removes a significant barrier to the modelling of epistemic uncertainties in industrial probabilistic safety analysis workflows.

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Uncertainty Management in Multidisciplinary Design of Critical Safety Systems

Cited by 38 publications

References 34 publications

Assessment of Micromechanically-Induced Uncertainties in the Electromechanical Response of MEMS Devices

Assessment of Micromechanically-Induced Uncertainties in the Electromechanical Response of MEMS Devices

Survival signature-based sensitivity analysis of systems with epistemic uncertainties

Analytic Probabilistic Safety Analysis under Severe Uncertainty

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