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The system reliability, the expected system life, and the expected number of failures are obtained under the assumption that the failure rate of a component depends on the number of consecutive failed components that follow it.
Historically, the failure rate for a constant failure phenomenon is estimated by using the Chi-square value or the expected number of failures.
Since the unreliability of systems with repairable components cannot be exactly calculated via fault tree, the Expected Number of Failures – which is obtained by integrating the unconditional failure frequency – is considered as it represents a good upper bound.
The purpose of this paper is to develop a new approach for computing various performance measures, namely reliability, availability, MTBF (mean time between failures), ENOF (expected number of failures), failure rate and repair time, for any industrial system.
Two alternative indicators of structural reliability are considered: (1) The expected number of failures over a time interval, taking into account the uncertainty due to both aleatory and epistemic uncertainties; (2) The confidence factor, λconf(t), and the confidence level, Kx t), as functions of time (t) are presented within a Demand-and-Capacity-Factor Design format (as used in FEMA 351).
Expected number of failures.
Similar(44)
Expected numbers of failures, repairs and replacements are evaluated by a probabilistic analysis using Latin hypercube sampling, and a novel approach is presented in order to smoothen these expected numbers w.r.t.
Expected numbers of failures and repairs are evaluated by Monte Carlo sampling, and a novel procedure is employed to evaluate sensitivities of the objective function with respect to design parameters.
Expected number of software failures detected by time t.
Notation m(t) Expected number of software failures detected by time t.
Expected number of software failures detected by time t, also known as mean value function.
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