An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties

Mi, Jinhua; Ning, Lu; Li, Yanfeng; Huang, Hong‐Zhong; Bai, Libing

doi:10.1016/j.ress.2021.108295

Cited by 61 publications

(32 citation statements)

References 37 publications

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“…And the classic AK‐IS method for small failure probability problem is used as a comparison to demonstrate the high efficiency of the proposed MRGP‐IS method. And the MCS method is adopted as a benchmark to verify the accuracy and thus its error for estimated failure probability is provided as 35

\mathrm{Error}=\frac{\mid {P}_f^{MCS}-{P}_f\mid }{P_f^{MCS}}\times 100\%,

where

{P}_f

is the estimated failure probability from the non‐MCS method and

{P}_f^{MCS}

is that from MCS.…”

Section: Several Examplesmentioning

confidence: 99%

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A novel approach for multi‐output structural system reliability problem with small failure probability

Qian

Huang

et al. 2022

Numerical Meth Engineering

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The multi-output structural system with implicit function widely exists in actual engineering, which refers that the multiple output responses of structural system can be obtained by one experiment or finite element simulation. Considering the correlation of multiple output responses and the small failure probability involved in multi-output structural system, this article proposes a novel active learning Kriging (ALK) based reliability method for multi-output structural system by combining multiple response Gaussian process (MRGP) and importance sampling (IS). First, due to the Kriging model can only construct the surrogate model under the single-output variable, the MRGP model is introduced to substitute the Kriging model and thus the correlation in multiple output responses can be efficiently described by a correlation matrix in MRGP model. Second, for the case that the distance information of new iterated sample point is not considered by the commonly used learning functions (U-function, EFF-function and H-function) in ALK, three improved learning functions are correspondingly proposed. Finally, aiming at the problem that the small failure probability leads to the increasing of candidate sample pool and further results in low computational efficiency, the IS method is combined with the MRGP model to efficiently accomplish the reliability analysis for multi-output structural system. Several examples are also provided to demonstrate the effectiveness of the proposed method.

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\mathrm{Error}=\frac{\mid {P}_f^{MCS}-{P}_f\mid }{P_f^{MCS}}\times 100\%,

where

{P}_f

is the estimated failure probability from the non‐MCS method and

{P}_f^{MCS}

is that from MCS.…”

Section: Several Examplesmentioning

confidence: 99%

“…And the MCS method is adopted as a benchmark to verify the accuracy and thus its error for estimated failure probability is provided as. 35 Error =…”

Section: Several Examplesmentioning

confidence: 99%

A novel approach for multi‐output structural system reliability problem with small failure probability

Qian

Huang

et al. 2022

Numerical Meth Engineering

View full text Add to dashboard Cite

show abstract

“…The increasing complexity of engineered systems brings various challenges to system reliability modelling and assessment. The first challenge is the complicated failure dependency among components, such as common cause failure modes, propagated failure modes, isolation failure modes, and cascading failure modes [4]. The second challenge is the rise of epistemic uncertainty associated with both the system reliability models and degradation model parameters [5].…”

Section: Introductionmentioning

confidence: 99%

“…The interval set theory [13] is a well-known method to quantify the epistemic uncertainty via using the lower and upper bounds of the model parameters. However, the interval set theory can only quantify the epistemic uncertainty while aleatory and epistemic uncertainties always coexist and couple together in engineering practices [4]. For example, in manufacturing systems, the length and thickness of the critical structures are stochastic because of manufacturing deviations and measurement errors while the estimation of the stochastic model parameters is imprecise due to limited data and data uncertainty [14].…”

Section: Introductionmentioning

confidence: 99%

“…Mi et al [3] developed another type of EN model by constructing the conditional belief table and conditional plausibility table to respectively calculate the system's belief reliability and plausible reliability. Recently, Mi et al [4] developed some special EN models that can handle both epistemic uncertainty of components' states and common cause failure (CCF). 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.…”

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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Reliability analysis for command‐and‐control systems based on edge extension diagram and binary decision diagram

Huang

Zhang

et al. 2022

Quality & Reliability Eng

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As the central nervous system of modern information warfare, the command‐and‐control network becomes the brain of command and the strategic target of the enemy's primary attack during the operation. In this paper, a method based on edge extension diagram (EED) and binary decision diagram (BDD) is proposed to evaluate the reliability of connectivity within the basic command post of command‐and‐control network, which is solving the problem that the conventional methods failed to model failure probabilities of node links and with low calculation efficiency. First, graph simplification of network topology, extension of nodes under reliable state, construction of path functions, replacement of associated edges, variable sorting, construction of binary decision graphs, and recursive calculation to evaluate the reliability of the connection between the two ends of the command‐and‐control network. Then, further analyze and sort the variable importance of the command‐and‐control network and analyze the sensitivity of the network topology. This paper contributes to the reliability assessment of command‐and‐control network system, the identification and maintenance of key network nodes, and the design of network topology structure.

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An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties

Cited by 61 publications

References 37 publications

A novel approach for multi‐output structural system reliability problem with small failure probability

A novel approach for multi‐output structural system reliability problem with small failure probability

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

Reliability analysis for command‐and‐control systems based on edge extension diagram and binary decision diagram

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