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It is based on terms such as performances, limit states, reliability and reference period or design service life.
For large systems with even a relatively small number of component states, reliability computation can become prohibitive or inaccurate using current methods.
Corollary 5 states reliability of ϱ ℓ, i.e. up to data oscillations ϱ ℓ provides an upper bound for the error.
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The serviceability limit state reliability analysis of piles is presented to illustrate the practical application of the proposed method.
Preliminary assessment of the device off-state reliability has been performed at elevated temperatures up to 310 °C.
Well known universal generating function technique also cannot be directly applied because this technique is primarily oriented to steady-state reliability analysis.
Due to drawbacks shown by Markovian model for steady state reliability computations and neural network for initial training pattern, integration being called Markov-neural is developed and evaluated.
The redundancy allocation problem is formulated with the objective of minimizing design cost, when the system exhibits a multi-state reliability behavior, given system-level performance constraints.
Two- and four-state reliability models that recognize the operating characteristics of base load units and peaking units are presented and compared in this study.
The reliability of a consecutive r-out-of-n: F system was obtained with a three-layer connected neural network represents a discrete time state reliability Markov model of the system.
Through multi-state reliability models provide more realistic and more precise representations of engineering systems, they are much more complex and present major difficulties in system definition and performance evaluation.
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