الفهرس 
introduction and motivationOnly 14 pages are availabe for public view
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Abstract
The increasing interest of recent research and studies in the subject of reliability came as a result of the role played by this science in dealing with the ages, whether of equipment or living organisms. the interest in reliability has increased after the wide spread of industry and the increase in the mechanical, electrical and electronic complexity of equipment in the last century. research before 1940 was limited to quality control and machine maintenance, and reliability was not identified at that time as a specific field. by the second world war and the increase in complex military equipment, the dependency field became a special entity and from it the modern dependency began. It is known that human thinking often involves vague information (fuzzy) that originally originated from accurate human concepts. since our systems are unable to answer many questions, fuzzy sets are able to provide solutions to many real-world problems. It is a powerful and well-known tool for the formulation and analysis of inaccuracies and subjectivity, as precise analysis is difficult or not possible. therefore, the aims of this thesis is to estimate the fuzzy reliability of the standard kumaraswamy distribution and the weibull distribution using three methods of estimation: the method of maximum likelihood, the method of percentiles estimations, and the standard bayes method using real data representing failure times of restorative dental ceramics using specific affiliation functions and reaching the best method by comparing with the mean squared error (MSE) criterion. through the application of the estimation methods, the superiority of the percentiles estimators method was found over the rest of the estimation methods for the standard kumaraswamy distribution, followed by the bayes method and finally the maximum likelihood method. As for the weibull distribution, the results showed the superiority of the bayes method in estimating the fuzzy reliability function, followed by the percentiles estimators method, and finally the maximum likelihood method.