A cost-based importance measure for system components: An extension of the Birnbaum importance

Wu, Shaomin; Coolen, Frank P. A.

doi:10.1016/j.ejor.2012.09.034

Cited by 77 publications

(32 citation statements)

References 23 publications

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“…Fangxing and Brown [21] proposed a new approach to prioritize distribution maintenance activities based on system reliability and cost-effectiveness. Shaomin et al [22] proposed a new cost based importance measure which considers costs incurred by maintaining a system and its components within a finite time horizon. Shubin et al [23] used the integrated importance measure to estimate the effect of a component residing at certain states on the performance of the entire multi-state systems.…”

Section: Introductionmentioning

confidence: 99%

FOR Allocation to Distribution Systems based on Credible Improvement Potential (CIP)

Tiwary¹,

Arya²,

Arya³

et al. 2016

J. Inst. Eng. India Ser. B

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This paper describes an algorithm for forced outage rate (FOR) allocation to each section of an electrical distribution system subject to satisfaction of reliability constraints at each load point. These constraints include threshold values of basic reliability indices, for example, failure rate, interruption duration and interruption duration per year at load points. Component improvement potential measure has been used for FOR allocation. Component with greatest magnitude of credible improvement potential (CIP) measure is selected for improving reliability performance. The approach adopted is a monovariable method where one component is selected for FOR allocation and in the next iteration another component is selected for FOR allocation based on the magnitude of CIP. The developed algorithm is implemented on sample radial distribution system.

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Section: Introductionmentioning

confidence: 99%

FOR Allocation to Distribution Systems based on Credible Improvement Potential (CIP)

Tiwary¹,

Arya²,

Arya³

et al. 2016

J. Inst. Eng. India Ser. B

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“…CBCI is introduced as the extension of Birnbaum importance [25]. In our paper CBCI of component i is defined as: where ∂C i (t) and ∂R i represent the increase of system cost and system reliability caused by reliability improvement of i-th component, respectively.…”

Section: Importance Measures That Consider Costs Of Componentsmentioning

confidence: 99%

A new approach for determining the most important system components and the budget-constrained system reliability improvement

Pavlović¹,

Makajić-Nikolić²,

Vujošević³

2017

EiN

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Article citation info:ponents' reliability on the overall system reliability is usually done by using importance measures. The concept of importance measures, originally introduced by Birnbaum [2] and Vesely [23], has been significantly changed and improved over time. Structural and probabilistic importance measures are used most often, but a small number of importance measures that take into account the costs of improving the reliability of the individual components have also been developed over the last few years [12,24]. A common feature of the majority of importance measures is that component's importance to the system functioning and failure is determined based on the calculated value of a chosen importance measure [27]. Therefore, the general procedure IntroductionImprovement of the reliability of a complex system, besides providing redundancy of components, is achieved by improving the reliability of the individual components that have the greatest impact on the overall reliability of the system. In this paper, we consider a complex system as a system with multiple interactions between many different components [15] in which the behavior of the system cannot be easily and directly induced from the behavior of its components [3]. Within the context of reliability, main characteristics of a complex system are: large number of components, cut sets, path sets or components' states dependencies [7]. Analysis of the impact of the com- PAvlović P, MAkAjić-Nikolić D, vujošević M. A new approach for determining the most important system components and the budget-constrained system reliability improvement. eksploatacja i Niezawodnosc -Maintenance and Reliability 2017; 19 (3): 413-419, http:// dx the number of the most important components which should be prevented is often limited due to the available budget. Using pre-known minimal cut sets, a mathematical model based on the Budgeted Maximum Coverage Problem is proposed. By its optimization, the simultaneous determination of all of the most important components whose total expenses do not exceed the limited overall budget is achieved. The new approach was tested by a series of experiments conducted over a set of test examples. The results of the experiments were compared with the results obtained using two commonly used cost importance measures. Keywords

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“…Levitin et al [13] generalized the importance measures for multi-state elements based on performance level restrictions. Wu and Coolen [24] extended the Birnbaum importance to a cost-based importance measure. Borgonovo et al [2,3] proposed differential importance measure, and time-independent reliability importance for the risk evaluation.…”

Section: Introductionmentioning

confidence: 99%

Generalized integrated importance measure for system performance evaluation: application to a propeller plane system

Dui¹,

Chen²,

Wu³

2017

EiN

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Generalized inteGrated importance measure for system performance evaluation: application to a propeller plane system uoGólniona miara zinteGrowanej ważności komponentów jako narzędzie oceny wydajności systemu: zastosowanie w odniesieniu do układu śmiGłowca : state vector of the components at time t ( ( )) X t Φ system structure function at time t and range {0,1, , [7,8] studied the IIM in system lifetime and semi-Markov process to evaluate the change of the system performance, respectively.The IIM evaluates the rate of system performance change due to a component changing from one state to another. The IIM simply considers the scenarios when the transition rate of a component from one state to another is constant. This may contradict the assumption of the degradation, based on which system performance is degrading and therefore the transition rate may be increasing over time.On the other hand, the Weibull distribution is one of the most commonly used lifetime distributions in reliability modeling and lifetime testing [25]. It has been used in many different engineering applications to model complex data sets, such as life tests [15], fault diagnosis [4,5], oral irrigators [16], et al. The Weibull distribution and its variants can accommodate increasing, constant or decreasing failure rates [10]. Thus, one may extend the IIM to a new importance measure that considers the scenarios where the transition rate of a component changing from one state to another as a time-dependent function. Typically, one may consider the conditional probability distribution of a component sojourning in a state is the Weibull distribution, given the next state that the component will jump to. On the basis of such consideration, this paper proposes a new importance measure. The research on the new importance measure can identify the most important component during three different time periods of the system lifetime, which is corresponding to the characteristics of Weibull distributions. The paper then derives some probabilistic properties. It also analyzes the properties of the proposed importance measure of the parallel-series systems and the series-parallel systems, respectively. A real-world example is borrowed to illustrate the proposed importance measure.The rest of the paper is as follows. Section 2 extends the IIM. The corresponding properties of IIM in the Weibull distribution are analyzed for typical parallel-series system and series-parallel system structures in Section 3. An application is presented to illustrate the proposed method in Section 4. Section 5 gives the conclusion of this paper. The extended IIM for system performance under Weibull distributionsIn this paper, the state space of component i is {0,1,…, M i } and the state space of the system is{0,1,…, M}. State 0 represents the complete failure state and state M (M i ) is the perfect functioning state. The states are ordered from the complete failure state to the perfect functioning state.We assume that the levels of maintenance cost and the system states ( 0 1) a...

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A cost-based importance measure for system components: An extension of the Birnbaum importance

Cited by 77 publications

References 23 publications

FOR Allocation to Distribution Systems based on Credible Improvement Potential (CIP)

FOR Allocation to Distribution Systems based on Credible Improvement Potential (CIP)

A new approach for determining the most important system components and the budget-constrained system reliability improvement

Generalized integrated importance measure for system performance evaluation: application to a propeller plane system

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