Some Recent Advances on Importance Measures in Reliability

Kuo, Way; Zhu, Xiaoyan

doi:10.1109/tr.2012.2194196

Cited by 128 publications

(55 citation statements)

References 86 publications

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“…Based on the network structure and probability parameters, the posterior probability of a target state is obtained given the state value of certain attribute node. Since NB supposes all variables are conditionally independent given the target variable, the joint probability distribution True, False Three-ratio method of oil chromatogram shows discharge V 3 True, False Core grounding current V 4 True, False Short-circuit impedance and load loss V 5 True, False Capacitance V 6 True, False No-load voltage ratio V 7 True, False No-load current and no-load loss V 8 True, False Direct current resistance V 9 True, False Absorptance and polarization index of insulation resistance V 10 True, False CO/CO 2 V 11…”

Section: Transformer Fault Diagnosis Based On Bnmentioning

confidence: 99%

“…Birnbaum importance (BI) λ B was proposed to represent the impact of a component on the reliability of a system when its state changed from one to another [10] . In this paper, BI presents the variation of certain fault posterior probability when the state of certain attribute changes from true to false.…”

Section: Transformer Fault Analysis Based On Importance Measuresmentioning

confidence: 99%

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Transformer fault analysis based on Bayesian networks and importance measures

Ren

Cai

et al. 2015

J. Shanghai Jiaotong Univ. (Sci.)

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Complex environment stresses bring many uncertainties to transformer fault. The Bayesian network (BN) can represent prior knowledge in the form of probability which makes it an effective tool to deal with the uncertain problems. This paper established a BN model for the transformer fault diagnosis with practical operation dataset and expert knowledge. Then importance measures are introduced to indentify the key attributes which affect the results of transformer diagnosis most. Moreover, a strategy was proposed to reduce the number of attribute in transformer fault detection and the resource cost was saved. At last, a diagnosis case of practical transformer was implemented to verify the effectiveness of this method.

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Section: Transformer Fault Diagnosis Based On Bnmentioning

confidence: 99%

Section: Transformer Fault Analysis Based On Importance Measuresmentioning

confidence: 99%

Transformer fault analysis based on Bayesian networks and importance measures

Ren

Cai

et al. 2015

J. Shanghai Jiaotong Univ. (Sci.)

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“…There are two major factors to consider when conducting importance analysis; the location and the reliability of components or subsystems [42]. In case of ADPSs, foregoing factors have vastly been discussed in [28,34].…”

Section: Scenario Bmentioning

confidence: 99%

The reliability investigation considering data uncertainty; an application of Fuzzy Transformation Method in substation protection

Aghili

Hajian-Hoseinabadi

2014

International Journal of Electrical Power & Energy Systems

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“…Thus, the importance measures can be classified as structural importance, reliability importance and lifetime importance measures (cf. [8][9][10]). Structural importance measures are defined based on the importance of the position of components in some senses and used when only the structure function of the system is known.…”

Section: Introductionmentioning

confidence: 99%

“…A variety of importance measures have been defined and used in the reliability literature, such as [8,[11][12][13][14][15][16][17][18][19][20][21]. For a comprehensive survey of measures of importance, see [22,9,10].…”

Section: Introductionmentioning

confidence: 99%

Importance of components in k-out-of-n system with components having random weights

Rahmani

Izadi

Khaledi

2016

Journal of Computational and Applied Mathematics

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a b s t r a c tThe purpose of this paper is to study the component importance in k-out-of-n system with components having random weights. The system works if at least k components work and the total weight of all working components is above the required level c. A new measure of component importance, called weighted importance, for such a system is introduced and its relation with Birnbaum reliability measure of importance is studied. It is shown that the larger weight stochastically implies the larger weighted measure of importance. It is also shown that if two components are identically distributed then the larger weight stochastically implies the larger Birnbaum reliability measure of importance. Finally, it is proved that if the weight of a component is stochastically increased, then the weighted and Birnbaum reliability measures of importance are increased accordingly.

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Some Recent Advances on Importance Measures in Reliability

Cited by 128 publications

References 86 publications

Transformer fault analysis based on Bayesian networks and importance measures

Transformer fault analysis based on Bayesian networks and importance measures

The reliability investigation considering data uncertainty; an application of Fuzzy Transformation Method in substation protection

Importance of components in k-out-of-n system with components having random weights

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