In this article, the fuzzy concepts are applied in analysis of the system reliability problem. The fuzzy number is used to construct the fuzzy reliability of the non-repairable multi-state series-parallel system (NMSS). The fuzzy failure rate function is represented by an exponential fuzzy number. By using this innovative approach, the fuzzy system reliability of NMSS is created. In order to analyse this fuzzy system reliability, the fuzzy Bayesian point estimate of fuzzy system reliability is made by the conventional Bayesian formula. And, the posterior fuzzy system reliability of NMSS is developed by Bayesian inference with fuzzy probabilities. Finally, the performance of the method is measured by the mean square error of fuzzy Bayesian point estimate for the fuzzy system reliability of NMSS.
The purpose of this paper is to create an interval estimation of the fuzzy system reliability for the repairable multistate series–parallel system (RMSS). Two-sided fuzzy confidence interval for the fuzzy system reliability is constructed. The performance of fuzzy confidence interval is considered based on the coverage probability and the expected length. In order to obtain the fuzzy system reliability, the fuzzy sets theory is applied to the system reliability problem when dealing with uncertainties in the RMSS. The fuzzy number with a triangular membership function is used for constructing the fuzzy failure rate and the fuzzy repair rate in the fuzzy reliability for the RMSS. The result shows that the good interval estimator for the fuzzy confidence interval is the obtained coverage probabilities the expected confidence coefficient with the narrowest expected length. The model presented herein is an effective estimation method when the sample size is n ≥ 100. In addition, the optimal α-cut for the narrowest lower expected length and the narrowest upper expected length are considered.
Predicting fire spread rates is essential in planning and deciding whether to conduct prescribed fires or suppressing forest fires. This study was conducted with the objective of developing a fire spread model for deciduous forest fires by using a simple statistical model. Test fires were conducted under a range of weather and fuel conditions to gather quantitative data on fire spreading. A series of 80 experimental fire plots were set in deciduous forests in the Northern Thailand during the forest fire seasons from [2008][2009]. The factors influencing the fire spread rate, i.e., weather, fuel, and topography conditions, were measured to model the fire spread. According to the burning experiments, the fire spread rate was 0.51-2.55 m/min. Multiple nonlinear regression analyses of slope terrain, fuel load, and moisture content of fuels were found to be able to accurately predict the fire spread rate at a confidence level of 25-88%. The developed model can be applied to deciduous forest fuels in other regions of Thailand. However, its use should be restricted to typical weather conditions.
Failure mode and effects analysis (FMEA) consists of the famous qualitative management methods used for improvements in management processes. This paper aims to determine the factors of defective products in the processing of poultry products in the industry. The causes of problems have been analyzed by systematic brainstorming of specialist consensus in the evaluation of problems to achieve unanimity on the violence level. The FMEA method uses the risk priority number (RPN), which indicates the priorities of risk problems and can evaluate three components: severity, occurrence and detection. Sometimes, this risk assessment leads to the wrong priorities. Therefore, we propose fuzzy FMEA methods for priority ranking of RPN and efficiently reducing poultry product defects, which are established based on fuzzy systems followed by comparison with conventional FMEA. The results indicate that the fuzzy FMEA method can efficiently and feasibly reduce poultry product defects.
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