Extended LK heuristics for the optimization of linear consecutive-k-out-of-n: F systems considering parametric uncertainty and model uncertainty

Qiu, Siqi; Sallak, Mohamed; Schön, Walter; Ming, Henry X.G.

doi:10.1016/j.ress.2018.01.016

Cited by 21 publications

(5 citation statements)

References 22 publications

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“…The second challenge is the rise of epistemic uncertainty associated with both the system reliability models and degradation model parameters [5]. The epistemic uncertainty of system reliability models refers to the failure dependency of components is not deterministic, and several candidates of system reliability models can be used to construct the system reliability function [6], [7]. The epistemic uncertainty of degradation model parameters arises when the time-tofailure data of the studied systems/components is limited and the estimated degradation model parameters are uncertain.…”

Section: Introductionmentioning

confidence: 99%

“…Xiahou et al [19], [20], [21] have published a series of works on system reliability modelling, redundancy allocation, and remaining useful life (RUL) prediction by using Dempster-Shafer theory to fuse multiplesource imprecise information. Qiu et al [6], [22] extended the component assignment problem (CAP) under Dempster-Shafer theory framework. CAP is a special reliability optimization problem aiming at maximizing system reliability by assigning the appropriate positions for components with different reliability functions.…”

Section: Introductionmentioning

confidence: 99%

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Reliability-Box of Systems Under Model Parameter Uncertainty Based on Evidential Variable and Evidential Network

Zhao

2022

IEEE Access

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Advanced engineering systems possess a large number of components with complicated failure dependencies. To accurately assess the system reliability, the degradation models of components should be known in advance and the model parameters should be accurately estimated via a large quantity of historical time-to-failure data. In real-world situations, due to limited data, lack of knowledge, and vague judgments from experts, components' degradation model parameters are, however, inevitably encountered with epistemic uncertainty and oftentimes quantified as evidential variables. In this article, upper and lower bounds of system reliability, termed as reliability-box, are estimated when components' degradation model parameters are elicited from experts and quantified by evidential variables. In the first place, the constrained optimization model is leveraged to assess the reliability-box of each component by giving the evidential variable of the component's degradation model parameters. Next, based on the system structure, the evidential network of the system is constructed to propagate the epistemic uncertainty from the component level to the system level. Therefore, the focal elements of the evidential variable of system reliability, i.e., the system reliability bounds, can be assessed via the belief and plausibility functions to the mass function of the leaf node of the evidential network. The effectiveness of the proposed methods is demonstrated by a rolling system in the chip cutting detection module.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliability-Box of Systems Under Model Parameter Uncertainty Based on Evidential Variable and Evidential Network

Zhao

2022

IEEE Access

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“…Some authors argued that the probability theory is not appropriate to distinguish aleatory and epistemic uncertainty, and they are strong proponents of those alternative methods. () Many efforts have been devoted to the reliability analysis of MSSs under epistemic uncertainty. () Liu and Huang studied the fuzzy MSSs when the transition rates and performance rates of MSEs are uncertain and/or imprecise and proposed a fuzzy continuous‐time Markov model with finite discrete states to model fuzzy MSSs in which transition rates and performance rates of elements are fuzzy values.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies only defined those operations for MSSs with performance sharing mechanism in the framework of probabilities. Because some authors claimed that the probability is not suitable to represent and distinguish aleatory and epistemic uncertainties,() this work uses the belief functions theory to represent these two kinds of uncertainties and defines those operations for MSSs with performance sharing mechanism in the framework of belief functions theory.…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of multi‐state series systems with performance sharing mechanism under epistemic uncertainty

Qiu

Ming

2019

Quality & Reliability Eng

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For real engineering systems, it is sometimes difficult to obtain sufficient data to estimate the precise values of some parameters in reliability analysis. This kind of uncertainty is called epistemic uncertainty. Because of the epistemic uncertainty, traditional universal generating function (UGF) technique is not appropriate to analyze the reliability of systems with performance sharing mechanism under epistemic uncertainty. This paper proposes a belief UGF (BUGF)-based method to evaluate the reliability of multi-state series systems with performance sharing mechanism under epistemic uncertainty. The proposed BUGF-based reliability analysis method is validated by an illustrative example and compared with the interval UGF (IUGF)-based methods with interval arithmetic or affine arithmetic. The illustrative example shows that the proposed BUGF-based method is more efficient than the IUGF-based methods in the reliability analysis of multi-state systems (MSSs) with performance sharing mechanism under epistemic uncertainty.

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“…This kind of uncertainty is referred to as the parametric uncertainty. () When the precise values of parameters are difficult to be obtained, sometimes, interval values of parameters are given instead of precise values. In this paper, the parametric uncertainty related to failure probabilities of components is taken into account by using interval values.…”

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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Extended LK heuristics for the optimization of linear consecutive-k-out-of-n: F systems considering parametric uncertainty and model uncertainty

Cited by 21 publications

References 22 publications

Reliability-Box of Systems Under Model Parameter Uncertainty Based on Evidential Variable and Evidential Network

Reliability-Box of Systems Under Model Parameter Uncertainty Based on Evidential Variable and Evidential Network

Reliability analysis of multi‐state series systems with performance sharing mechanism under epistemic uncertainty

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

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