Reliability Analysis of Load-Sharing<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:math>-out-of-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:math>System Considering Component Degradation

Yang, Chaojie; Zeng, Shengkui; Guo, Jianbin

doi:10.1155/2015/726853

Cited by 8 publications

(6 citation statements)

References 22 publications

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“…Two alternate approaches have been taken in the literature for analyzing the reliability function of a load-sharing system. The first one has employed the strength degradation model [22,33,34,39,41], whereas the second one has assumed a given lifetime distribution with either selecting a tampered failure rate model (TFRM) or a cumulative exposure model (CEM) for modeling the effect of the load history on the lifetime distribution [11,18,21,[28][29][30]36]. While most of the literature has considered an exponential component lifetime distribution [13,18,[28][29][30]36], only numerical examples were given for calculating the reliability of a system composed of two components when components follow any other lifetime distributions because the reliability function has no closed form [11,21,39].…”

Section: Load-sharing Systemmentioning

confidence: 99%

Reliability Comparison of Two Unit Redundancy Systems Under the Load Requirement

Kim¹

2020

Prob. Eng. Inf. Sci.

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This paper compares the reliability functions of the cold standby, hot standby, and load-sharing redundancy configurations, each of which is composed of two identical components for meeting a given system requirement. Thus far, no research has been done into the conditions that make one configuration more reliable than another because their reliability functions have no closed forms even when the component follows a Weibull lifetime distribution. In this paper, two analytical results are obtained given that the reliability of each configuration is expressed in terms of the design and operational loads of the component. First, higher reliability can be achieved in a cold standby configuration than in a load-sharing configuration if the increase in the component reliability obtained from the reduction in the operational load is not significant. Second, a cold standby configuration exhibits better reliability and carries a higher load than a hot standby configuration if the design load can be increased with a less decrease in the component reliability.

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Section: Load-sharing Systemmentioning

confidence: 99%

Reliability Comparison of Two Unit Redundancy Systems Under the Load Requirement

Kim¹

2020

Prob. Eng. Inf. Sci.

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“…As the primary cause of component degradation, the cumulative work load was adopted and exhibited an inverse Gaussian distribution. Yang et al [17] proposed a method that combined a tampered failure rate model with a performance degradation model to analyze the reliability of a load-sharing k-out-of-n system with degrading components. Liu et al [3] constructed reliability models for load sharing systems with degrading components, and showed that constant and varying load have a cumulative impact on the system.…”

Section: A Overview Of Load Sharing Rules and Failure Type And Distrmentioning

confidence: 99%

A Failure Mechanism Cumulative Model for Reliability Evaluation of a k-Out-of-n System With Load Sharing Effect

Chen

2019

IEEE Access

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In this paper, we propose a failure mechanism cumulative model that considers the loading history of the load sharing effect in a k-out-of-n system. Three types of failure mechanisms are considered, continuous degradation, compound point degradation, and sudden failure due to shock. By constructing a logic diagram with functional dependence gate, the load-sharing effect can be explained from the failure mechanism point of view using a mechanism-acceleration gate that shows that when one component fails, the failure mechanisms of the other surviving components will be accelerated. By deriving the total damage equation and a constructing failure behavior model, the system reliability of a k-out-of-n system with different types of failure mechanisms were evaluated. A voltage stabilizing system that contains a 1-out-of-2 subsystem, or a 2-out-of 3 subsystem was used to illustrate the practical applicability of the proposed approach. A combined Monte Carlo and binary decision diagram method was used in the numerical simulation process. INDEX TERMS K-out-of-n system, load sharing effect, failure mechanism cumulative model, functional dependence, acceleration.

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“…Each component in the system operates at the lowest load when all components survive, and any component failure induces a higher load in the surviving components. 1 A considerable number of papers have developed reliability models for a load-sharing system, based on either the accelerated life test model [2][3][4][5][6] or the strength degradation model [7][8][9][10][11][12] to consider the effect of different operational loads on the component reliability, under the assumption that the system load requirement is equally shared by its identical components. The component operating at the lowest load serves as a baseline in evaluating the system reliability, and thus the baseline changes if the number of components in the system changes.…”

Section: Introductionmentioning

confidence: 99%

Reliability maximization of a load‐sharing system without redundant components

Kim

Park

Hong

2022

Quality & Reliability Eng

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This paper considers a problem of determining the optimal number of components for maximizing reliability of a load‐sharing series system in which no redundant components exist. Previously, this problem has been studied numerically, under the assumption that the load–life relationship is described by a power function, and the component follows an exponential or a Weibull lifetime distribution. In this paper, our objective is to obtain analytical results for the optimal number of components given that a general link function explains a load–life relationship and the component follows a general lifetime distribution. We show that the optimal number of components non‐decreases with the elasticity of the link function and the ratio of the component failures sensitive to the changing design load. We also show that the premature failures occurring early in the life of a component affect the optimal solution, depending on whether a link function is used for scaling the nominal failure rate independently of the mission time, or it is used for scaling the nominal life dependently with the mission time. We believe that the result is useful in selecting the number of the components to use in a system when studying design trade‐offs at an early design stage.

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Reliability Analysis of Load-SharingK-out-of-NSystem Considering Component Degradation

Cited by 8 publications

References 22 publications

Reliability Comparison of Two Unit Redundancy Systems Under the Load Requirement

Reliability Comparison of Two Unit Redundancy Systems Under the Load Requirement

A Failure Mechanism Cumulative Model for Reliability Evaluation of a k-Out-of-n System With Load Sharing Effect

Reliability maximization of a load‐sharing system without redundant components

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