The main objective of fault tree analysis method is to estimate the “Top Event occurrence probability”. This requires determination of failure time distribution functions also known as “Bathtub Curves” for each of the system elements/events. This paper introduces a novel method to determine the failure time distribution functions using possibility theory. For this purpose, fuzzy‐bathtub distributions using expert opinions are generated for basic events and fuzzy formulas are derived for static and dynamic gates fault tree constructions. This process completed by proposed fuzzy Monte Carlo simulation throughout the preferred operational time and uses the actual time‐to‐failure data. Accordingly, the Top Event failure curve and the reliability profile of the system are depicted based on the defuzzificated basic‐events' bathtub‐failure‐rates. The results show that the proposed method not only is feasible and powerful but can also be accurate more than the other probabilistic and possibilistic techniques because of the component failure rates follow the real failure distributions.
The use of capacitor banks in distribution system has many outstanding usages include improving the power factor of a system, voltage profile, and reliability besides the reducing of the power flow losses of the component's reactive due to the compensation. These benefits depend greatly on how capacitors are placed in the distribution system. Hence, in order to achieve the high reliable construction, switching capacitor has been placed to improve the main challenges of the network designing (reliability and reduce power loss) in the radial distribution system. As regards, the importance of the reliability and power losses are ignored in the distribution networks; the aim of this paper is primarily to establish an objective function for the parallel optimization of these aforementioned parameters. In the simulation process, ten parameters have been compared, which are: System Average Interruption Frequent Index (SAIFI) and its cost, System Average Interruption Duration Index (SAIDI) and its cost, power loss and its cost, the installed capacity and it's cost and values of two objective functions. Honey-bee mating optimization (HBMO) algorithm has been used to solve this problem. Then, the developed technique has been used on the IEEE standard distribution network as a problem-solving system.
The Duane and Crow-AMSAA reliability growth model has been traditionally used to model systems and products undergoing development testing. The Non-Homogeneous Poisson Process (NHPP) with a power intensity law has been often used as a model for describing the failure pattern of the repairable systems and the maximum likelihood (ML) estimates are used to calculate the unknown parameters widely. This study proposes the statistical analysis method of different stages and different level data based on Bayes analysis techniques. To this end, the Bayesian reliability growth model of multiple stages is coupled with the Weibull distribution product. By using the unique properties of the assumed prior distributions, the moments of the posterior distribution of the failure rate at various stages during a development test can be found. In this paper, it is assumed that the scale parameter has a Gamma prior density function, and the growth parameter has a Uniform prior distribution. Monte Carlo simulations are used to compute the Bayes estimates. Finally, the results obtained from the proposed method by implementing it on an application example are compared with Crow-AMSAA data and show that the proposed model has higher accuracy than the existing traditional methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.