A discretizing approach for evaluating reliability of complex systems under stress-strength model

Roy, Dilip; Dasgupta, Tanmoy

doi:10.1109/24.963121

Cited by 36 publications

(33 citation statements)

References 15 publications

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“…Where the parameter 0 < δ < 1 is so chosen that the first two raw moments of X and Y remains close (Roy and Dasgupta 2001). Except for a shift in the location by δ the pmf in Eq.…”

Section: Methodology-vmentioning

confidence: 99%

Generating discrete analogues of continuous probability distributions-A survey of methods and constructions

Chakraborty

2015

J Stat Distrib App

117

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“…Where the parameter 0 < δ < 1 is so chosen that the first two raw moments of X and Y remains close (Roy and Dasgupta 2001). Except for a shift in the location by δ the pmf in Eq.…”

Section: Methodology-vmentioning

confidence: 99%

Generating discrete analogues of continuous probability distributions-A survey of methods and constructions

Chakraborty

2015

J Stat Distrib App

117

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“…However, most of reliability models were derived directly through the load-strength interference model and they can't embody the relationship between reliability and the number of load application essentially, namely, they can only calculate the reliability when random load is applied for one or the specified times [1][2][3][4][5][6][7][8][9][10]. In this section, we present the reliability models of components when the life measured by the number of load application.…”

Section: Reliability Model Of Components Under Repeated Random Loadmentioning

confidence: 99%

Failure rate model of components with the number of load application as life parameter

Wang

Kang

Xie

2009

2009 8th International Conference on Reliability, Maintainability and Safety

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The failure rate model of components with the number of load application as life parameter is developed in this paper. Taking static strength failure and fatigue failure as the backgrounds, the dynamic reliability models of components under random repeated load without strength degradation and those with strength degradation are derived, respectively. Then, the failure rate model of components with the number of load application as life parameter is derived without strength degradation and those with strength degradation are derived, respectively. The relationship between reliability and the number of load application, and that between failure rate and the number of load application are discussed in different cases. The result shows that even though when strength doesn't degenerate, both reliability and failure rate of components decrease as the number of load application increases, and the failure rate curve has the partial character of bathtub curve with early failure period and random failure period. When strength degenerates with the number of load application, the reliability of components decreases more obviously, and the failure rate curve of components is bathtub-shaped.Keywords-failure rate; reliability model; the number of load application; load-strength interference

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“…In the two last decades, intensive researches have been carried out to provide methods to solve (1), a (non exhaustive probably) list of main approaches to solve (1) is: direct integration of PDF on the failure domain, Monte-Carlo simulations [7] (including subsets simulations [8]), FORM/SORM using response surface method or the reliability index [1,8], discrete approximation [10].…”

Section: Statement Of the Problemmentioning

confidence: 99%

Structural reliability assessment based on particles swarm optimization

Elegbede¹

2005

Structural Safety

125

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A discretizing approach for evaluating reliability of complex systems under stress-strength model

Cited by 36 publications

References 15 publications

Generating discrete analogues of continuous probability distributions-A survey of methods and constructions

Generating discrete analogues of continuous probability distributions-A survey of methods and constructions

Failure rate model of components with the number of load application as life parameter

Structural reliability assessment based on particles swarm optimization

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