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FAILURE DISTRIBUTION AT COMPONENT LEVEL

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Paper number  033003,  2003 ASAE Annual Meeting . (doi: 10.13031/2013.14043) @2003
Authors:   Raquel Salazar Moreno, Abraham Rojano Aguilar
Keywords:   Reliability, Time to Failure, Weibull Distribution, Engines

High quality in systems and products is a wide term which include : reliability, initial performance, easy to use, security and compatibility under different environments. The most important characteristic of quality is reliability because of its impact in productivity, maintenance and costs. This paper shows the failure distribution estimation at component level for a non repairable item(considering only first failure) using Matlab. Failure distribution is important for reliability prediction and optimal maintenance policy. Although reliability prediction is important in the early stages of the design is also important in studies of maintenance requirements and reliability growth. The failure data for mechanical components sample was recorded and then the statistical analysis was done using Minitab, fitting the best statistical model between Normal, Lognormal, Exponencial and Weibull distribution. The minimum Anderson Darling coeficient was found for Weibull Distribution. Because the Minitab program assume the location parameter in zero which can deal to a wrong conclusion, a program for nonlinear programming was written in Matlab to find the parameters for Weibull distribution. The three parameters founded were =3.6 , =3284.5, =1140.8. The common functions in reliability engineering were generated: failure density function, reliability function, mean time between failures, and failure rate function. The reliability estimation at any given stage as well as prediction of future reliability can be accomplished once the failure distribution function has been calculated . The statistical models not only represent the system , but enable the manager to assess progress and evaluate the quality of a product at a given point in time.

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