Detail publikace

FReET: Software for the statistical and reliability analysis of engineering problems and FReET-D: Degradation module

Originální název

FReET: Software for the statistical and reliability analysis of engineering problems and FReET-D: Degradation module

Anglický název

FReET: Software for the statistical and reliability analysis of engineering problems and FReET-D: Degradation module

Jazyk

en

Originální abstrakt

The objective of the paper is to present methods and software for the efficient statisticial, sensitivity and reliability assessment of engineering problems. Attention is given to small-sample techniques which have been developed for the analysis of computationally intensive problems. The paper shows the possibility off randomizing computationally intensive problems in the manner of the Monte Carlo type of simulation. In order to keep the number of required simulations at an acceptable level, Latin Hypercube Sampling is utilized.

Anglický abstrakt

The objective of the paper is to present methods and software for the efficient statisticial, sensitivity and reliability assessment of engineering problems. Attention is given to small-sample techniques which have been developed for the analysis of computationally intensive problems. The paper shows the possibility off randomizing computationally intensive problems in the manner of the Monte Carlo type of simulation. In order to keep the number of required simulations at an acceptable level, Latin Hypercube Sampling is utilized.

BibTex


@article{BUT105877,
  author="Drahomír {Novák} and Miroslav {Vořechovský} and Břetislav {Teplý}",
  title="FReET: Software for the statistical and reliability analysis of engineering problems and FReET-D: Degradation module",
  annote="The objective of the paper is to present methods and software for the efficient statisticial, sensitivity and reliability assessment of engineering problems. Attention is given to small-sample techniques which have been developed for the analysis of computationally intensive problems. The paper shows the possibility off randomizing computationally intensive problems in the manner of the Monte Carlo type of simulation. In order to keep the number of required simulations at an acceptable level, Latin Hypercube Sampling is utilized.",
  chapter="105877",
  doi="10.1016/j.advengsoft.2013.06.011",
  howpublished="online",
  number="2014",
  volume="72",
  year="2014",
  month="june",
  pages="179--192",
  type="journal article in Web of Science"
}