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Content available remote A study of health management of LWD tool based on data-driven and model-driven
EN
Electromagnetic wave logging-while-drilling (LWD) tool plays an important role in unconventional oil and gas exploitation and deep-sea oil and gas resource exploration process. The reliability such as reliable life and durability of the tool can control drilling efficiency and production cost in extreme environmental conditions. In this paper, main faults of the electromagnetic wave LWD tool have been analyzed when it working to the drilling site. Failure time of antenna coils, circuit boards, and power supply have been recorded. Therefore, failure mode and failure mechanism can be analyzed of the tool. Secondly, a fault analysis model of electromagnetic wave LWD tool based on Weibull distribution model has been built up, and by using this fault analysis model the reliable life and the remaining useful life of antenna system can be calculated. The last, the goodness-of-ft test can be operated to Weibull distribution model by using Kolmogorov–Smirnov test. Study results show that the reliability and the law of fault occurrence of electromagnetic wave LWD tool can be directly reflected. And it has practical significance to reliability evaluation of the instrument system and joint optimization of safe operation and maintenance of the tool.
EN
This paper derives an inventory model is developed for items that deteriorates at a generalized Weibull distributed rate when demand for the items is dependent on the selling price. Shortages are not allowed and price inflation is taken into consideration over finite planning horizon. A brief analysis of the cost involved is carried out by theoretical analysis.
PL
Praca przedstawia opracowanie modelu zarządzania zapasem dla towarów, które ulegają zużyciu zgodnie z modelem Weibulla, i których popyt zależny jest od ceny sprzedaży. Braki nie są dozwolone. Czynnik inflacji został uwzględniony dla określonego horyzontu czasowego. Została przeprowadzona krótka teoretyczna analiza kosztów.
EN
Wind speed is receiving greater attention in the design and study of wind energy conversion systems (WECS). Using meteorological data, this paper studies the availability of wind energy potential at four sites in Ireland: Malin Head, Dublin Airport, Belmullet and Mullingar. An analysis is made of mean wind speed data collected at a height of 50 m above ground level at each site over a period of seven years. A two parameter Weibull distribution model is used to analyze wind speed pattern variations. Weibull parameters are calculated by the Least Squares Method (LSM). The results relating to wind energy potential are given in terms of the density function. Analysis shows that coastal sites of Ireland such as Malin Head, Dublin Airport and Belmullet have good wind power potential.
EN
In this paper, we present novel closed-form expressions for the probability density function (PDF) and cumulative distribution function (CDF) of ratio of random variable and product of two random variables for the cases where random variables are Rayleigh, Weibull, Nakagami-m and [alfa]-[my] distributed. An application of obtained results in performance analysis of multi-hop wireless communication systems in different transmission environments is detailed described. The proposed theoretical analysis is also complemented by various graphically presented numerical results.
PL
W artykule zaprezentowano wyrażenia na funkcję gęstości prawdopodobieństwa PDF i kumulacyjną funkcję dystrybucji stosunku zmiennej przypadkowej i iloczynu dwóch zmiennych, gdy zmienne przypadkowe są dystrybucją Rayleigh, Weibull, Nakagami-m i [alfa]-[my]. Analiza ma zastosowanie do bezprzewodowych sieci komunikacyjnych w różnych warunkach transmisji.
EN
In the paper a classical model of failures is considered in that successive failurefree times are supposed to have Weibull distributions and are followed by exponentially distributed times of repairs. It is assumed that parameters of these distributions, in general, change with time. Basing on information about the number of failures, failure-free times, repair times, in a number of periods of the same duration in the past parameters of the model are estimated. Next, predictions of the most important reliability characteristics are found using classical regression technique.
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