Transferable belief model for reliability analysis of systems with data uncertainties and failure dependencies

Sallak, Mohamed; Schön, Walter; Aguirre, Felipe

doi:10.1243/1748006xjrr292

Cited by 16 publications

(20 citation statements)

References 39 publications

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“…The second point of evidence theory is that belief measures of uncertainty may be assigned to overlapping sets and subsets of hypotheses, events or propositions as well as to individual hypothesis. D-S theory which can be considered as an alternative approach to represent uncertainties has gained an increasing amount of attention both from the theoretical and the applied point of view [30], [31], [32], [33]. In a finite discrete space, D-S theory is a generalization of probability theory where probabilities are assigned to sets instead of mutually exclusive singletons.…”

Section: Bba Basic Belief Assignmentmentioning

confidence: 99%

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Extended Component Importance Measures Considering Aleatory and Epistemic Uncertainties

2013

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In this paper, extended component importance measures (Birnbaum importance, RAW, RRW and Criticality importance) considering aleatory and epistemic uncertainties are introduced. The D-S theory which is considered to be a less restricted extension of probability theory is proposed as a framework for taking into account both aleatory and epistemic uncertainties. The epistemic uncertainty defined in this paper is the total lack of knowledge of the component state. The objective is to translate this epistemic uncertainty to the epistemic uncertainty of system state and to the epistemic uncertainty of importance measures of components. The Affine Arithmetic allows us to provide much tighter bounds in the computing process of interval bounds of importance measures avoiding the error explosion problem. The efficiency of the proposed measures is demonstrated using a bridge system with different types of reliability data (aleatory uncertainty, epistemic uncertainty and experts' judgments). The influence of the epistemic uncertainty on the components' rankings is described. Finally, a case study of a fire-detector system located in a production room is provided.A comparison between the proposed measures and the probabilistic importance measures using two-stage Monte Carlo simulations is also made.

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Section: Bba Basic Belief Assignmentmentioning

confidence: 99%

“…Nevertheless, there is very few works concerning the construction of BBAs [41], [33]. The problem of elicitation of experts' judgments has long been addressed in the probability theory framework related to reliability and risk assessments.…”

Section: Construction Of Bbasmentioning

confidence: 99%

Extended Component Importance Measures Considering Aleatory and Epistemic Uncertainties

2013

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“…40 In recent years, the belief functions theory was used by many researchers in order to quantify the uncertainty in reliability and risk assessment studies. 34,35,[41][42][43][44][45] …”

Section: Belief Functions Theorymentioning

confidence: 99%

“…Dempster-Shafer (D-S) theory, which can be considered as an alternative approach to represent uncertainties, has gained an increasing amount of attention both from the theoretical and the applied point of view. [32][33][34][35] In a finite discrete space, D-S theory is a generalization of probability theory where probabilities are assigned to sets instead of mutually exclusive singletons. This theory is still a young field compared with other theories and its main application is data fusion.…”

Section: Introductionmentioning

confidence: 99%

Application of evidential networks in quantitative analysis of railway accidents

Aguirre

Sallak

Schön

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part O:

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Currently, a high percentage of accidents in railway systems are accounted to human factors. As a consequence, safety engineers try to take into account this factor in risk assessment. However, human reliability data are very difficult to quantify, thus, qualitative methods are often used in railway system's risk assessments. Modeling of human errors through probabilistic approaches has shown some limitation concerning the quantification of qualitative aspects of human factors. The proposed article presents an original method to account for the human factor by using evidential networks and fault tree analysis.

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“…In their work, an OR gate was added explicitly to connect the original failure event in the FT model and the propagated failure events affected by the original failure event; then, these FT models are transformed into BDDs to evaluate the system unreliability. Sallak et al proposed explicit and implicit transferable belief model–based methods to analyze propagated failures (also called failure dependencies between components) in systems considering both epistemic and aleatory uncertainties. In their work, uncertainties related to reliability data were represented and propagated by belief functions theory, and propagated failures were represented by conditional basic probability assignments.…”

Section: Introductionmentioning

confidence: 99%

Binary decision diagram–based methods for risk assessment of systems subject to propagated failures and parametric uncertainties

Qiu

Ming

2018

Quality & Reliability Eng

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Propagated failures between components mean that the failure of one component may cause the failures of other components within the system. Because propagated failures contribute greatly to the system unavailability, it is important to take into account propagated failures between components when modeling systems. Sometimes, it may be difficult to obtain the precise values of some parameters due to many reasons, such as the insufficiency of historical data and the randomness of statistical data. In this case, interval values can be given instead of precise values to represent the parametric uncertainty related to the values of parameters. This paper proposes explicit and implicit binary decision diagram–based methods to model systems subject to propagated failures and to evaluate the failure probability of systems under parametric uncertainty. The proposed methods are applied to a near head‐to‐head railway accident to evaluate the occurrence probability of accidents.

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Transferable belief model for reliability analysis of systems with data uncertainties and failure dependencies

Cited by 16 publications

References 39 publications

Extended Component Importance Measures Considering Aleatory and Epistemic Uncertainties

Extended Component Importance Measures Considering Aleatory and Epistemic Uncertainties

Application of evidential networks in quantitative analysis of railway accidents

Binary decision diagram–based methods for risk assessment of systems subject to propagated failures and parametric uncertainties

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