Raquel Salazar Moreno

FAILURE DISTRIBUTION AT COMPONENT LEVEL

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 â=3.6 , c=3284.5, a=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.

Artificial Neural Network as a Prediction Tool in Agricultural Variables

Artificial Neural Network (ANN) technology is a form of artificial intelligence that learns by processing representative data patterns through its internal architecture. ANN technology often offers a superior alternative to traditional physical-based models, and excel at uncovering patterns or relationships in data. It is also a powerful non-linear estimator which is recommended when the functional form between input-output is unknown or it is not well understood but it is believed could be nonlinear. This paper show two applications of ANN for forecasting solar radiation and available dam storage. In the first case ANN provided a good accurate forecasts for all period with an average square error of 0.05% in the prediction. For the second case, ANN provide relatively accurate estimates of water availability one month into the future in the Purisima dam located in the state of Guanajuato. The results suggest that additional input variables such as runoff, cropping patterns and groundwater extractions may be necessary to increase ANN forecasting accuracy. This feasibility study demonstrates that ANN technology has the potential to serve as a highly accurate forecasting tool. Moreover, ANN technology can continuously be updated, as new data become available, increasing its forecasting ability.