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This paper proposes a elaboration of a Fault Tree Analysis (FTA) for gearboxes to study reliability of statistical models more adequate for components which may be able to generate failures. Therefore there was generated a fault tree with scale and shape factors for each component. Once obtained the statistical models for each failure, a study about critical levels of the system components will be valid by reduction of the FTA models. The aim of the actions in relation to the improvements will increase the reliability of the gearboxes. This method can be applied in various vehicles systems allowing a complete statistic modeling. Thus, a general map of the product can be made, in which there are components that generate or will be able to generate failures in specific kilometer. With this, it is possible to obtain a model for management of the actions of warranty and descriptions for the areas of product development and manufacturing.

This work presents an application of the simulated annealing algorithm to redundant system reliability optimization, and its objective is to analyze this method and compare to other optimization methods, which are Genetic Algorithm, Evolution Strategy and Lagrange Multipliers. A hidrid optimization algorithm, composed by Simulated Annealing and Genetic Algorithm, is also developed in this work, to conciliate the advantages of both methods. The results presented by the Simulated Annealing and the hybrid algorithm are significant and validate these methods as robust tools for parameters optimization in engineering systems developments

The dependence among the components and the data associated with a repair politic, that can consists of the number of available teams and the components repair priority, rarely evaluate the measures of reliability. Thus, to evalute the reliability and availability values with these conditions, this article develops a methodology using the theory of Markovian stochastic processes. A system of ordinary differential equations is obtained using the concepts of component failure and repair rates, constants or variable in the time. Solving it, these values are obtained, allowing the calculation of the MTTF of the analyzed system, for an allowed error.

The reliability optimization of redundant systems aims to improve some design parameters as reliability or cost, using optimization methods, in order to determine the optimum number of redundancies in each component of the system. This work compares some optimization methods (Lagrange Multipliers, Evolution Strategy and Genetic Algorithm) and its objective is to determine which method is more adjusted to the problem.