Evidence-based quantification of uncertainties induced via simulation-based modeling

Riley, Matthew E.

doi:10.1016/j.ress.2014.08.016

Cited by 20 publications

(9 citation statements)

References 21 publications

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“…Both regarding model behavior and model output, databased model validation is preferred, i.e. comparing model generated data with realistic data [79], [80], [81]. Typically, air traffic conflict and collision model applications apply databased validation to their sub-models.…”

Section: Model Validationmentioning

confidence: 99%

“…Sensitivity analysis (SA) aims to measure how sensitive the output of the entire model is to single and joint changes in model parameter values [82]. Uncertainty quantification (UQ) aims to estimate the levels of uncertainty in the output of the model as a result of aleatory and epistemic uncertainties in the parameters of the model and of potential differences between model and ATM operation (design) considered [83], [80], [81]. Because the level of uncertainty at the output of the model is the product of the level of uncertainty at the input multiplied by the sensitivity of the model, SA and UQ form two sides of one coin.…”

Section: Model Validationmentioning

confidence: 99%

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Mathematical Models for Air Traffic Conflict and Collision Probability Estimation

Mitici

Blom

2019

IEEE Trans. Intell. Transport. Syst.

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Increasing traffic demands and technological developments provide novel design opportunities for future Air Traffic Management (ATM). In order to evaluate current air traffic operations and future designs, over the past decades several mathematical models have been proposed for air traffic conflict and collision probability estimation. However, few comparative evaluations of these models with respect to their mathematical core exist. Such comparative evaluations are particularly difficult since different authors employ different model definitions, notations and assumptions, even when using the same modeling techniques. The aim of this paper is: i) to present the mathematical core of the existing approaches for air traffic conflict and collision probability estimation using the same body of notations and definitions; ii) to outline the advances in estimating the probability of air traffic conflict and collision using a unified mathematical framework; iii) to various air traffic applications and their use of directed mathematical models for air traffic conflict and collision probability estimation; and iv) to provide insight into the capabilities and restrictions of the mathematical models in the evaluation of future ATM designs.

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Section: Model Validationmentioning

confidence: 99%

Section: Model Validationmentioning

confidence: 99%

Mathematical Models for Air Traffic Conflict and Collision Probability Estimation

Mitici

Blom

2019

IEEE Trans. Intell. Transport. Syst.

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“…where n M is the candidate model size. The model prior probabilities for Gamma distribution model and Weibull distribution model in this application are given by equations (27) and (28)…”

Section: Model Prior Probabilitymentioning

confidence: 99%

“…where M 1 means the Gamma distribution model, M 2 means the Weibull distribution model, p(M) is the priors of the model probabilities, equations ( 27) and (28), p(u k M k j ) is the priors of the distribution model parameters u k , equations ( 33)- (40), (29) and (35), ft E g is the lifetime data, and p(u, M t E i…”

Section: Parameters Estimationmentioning

confidence: 99%

“…However, the model selecting criterions cannot be used to handle the model uncertainty, especially for the small lifetime data conditions. 26 Generally, the model uncertainty can be handled by evidence theory, 27,28 adjustment factor method, 29 and model averaging method. 30,31 The model averaging method is always combined with Bayesian inference method and extended into the Bayesian model averaging (BMA) method in the reliability engineering.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian model averaging based storage lifetime assessment method for rubber sealing rings

Liu

Wang

Zhang

et al. 2019

Advances in Mechanical Engineering

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Rubber sealing ring is one of the most widely used seals. It is always stored for a period of time before put into use, especially in aeronautic and aerospace applications. It is necessary to evaluate the storage lifetime of rubber sealing rings. However, due to the long storage lifetime of rubber sealing rings, two issues need to be handled, including model uncertainty and lack of storage lifetime data. A Bayesian model averaging based storage lifetime assessment method for rubber sealing rings is proposed in this article. The Gamma distribution model and Weibull distribution model are selected as the candidate models and combined based on Bayesian model averaging method. The Bayesian model averaging method is applied to handle the model uncertainty. Considering the lack of storage lifetime data, the degradation data are utilized to give the priors of model probability and distribution parameters based on the similarity principle. The results indicate that the proposed method has smaller minus log-likelihood value and is better than the other discussed method, considering both goodness of fit and complexity.

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A reliability estimation method based on two‐phase Wiener process with evidential variable using two types of testing data

Shi

Qiao

Wang

et al. 2022

Quality & Reliability Eng

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The evidential variable shows the powerful ability on the parameter description, so we have cooperated it with the linear Wiener process to model linear degradation processes. However, the degradation paths of some products exhibit two-phase patterns over the running time, which cannot be modeled by the linear Wiener process. Hence, it is developed into an associated evidential variable-based two-phase Wiener process. In order to utilize the proposed model, the related evidence obtaining, evidence fusion and performance inference approaches are constructed. A simulation study and an actual case are performed to verify the superiorities of the proposed method. The proposed method provides higher accuracies of the useful life, degradation, and reliability evaluations, due to utilizing the evidential variable and considering the two-phase degradation conditions.

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Evidence-based quantification of uncertainties induced via simulation-based modeling

Cited by 20 publications

References 21 publications

Mathematical Models for Air Traffic Conflict and Collision Probability Estimation

Mathematical Models for Air Traffic Conflict and Collision Probability Estimation

Bayesian model averaging based storage lifetime assessment method for rubber sealing rings

A reliability estimation method based on two‐phase Wiener process with evidential variable using two types of testing data

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