Abraham Rojano Aguilar

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.

Design of a Piglet Resting Area

The physical, social and thermal factors that provide acceptable animal welfare in production units depend on the environmental conditions in its housing. The thermal comfort is perhaps the most important factor in the first hours of life. In maternity rooms of a pig production system, one must satisfy the biological needs with the piglets (38°C), the sow (20 °C) and people (20 °C). That is to say, the welfare of the sow must be compatible with the rate of survival of the piglets. This paper proposes the design of a rest area for piglets (lechonera) that provides for thermal comfort of the piglets and sow. We analyzed the natural behavior and biological exigencies of the piglets and functionality of a farrowing crate. The walls, ceiling and floor are composed of three layers, Wood-plastic bubble-wood, to have a total coefficient of resistance to heat passage of 0,19 W m-2 K-1. The floor is heated to 32°C to provide the heat necessary to maintain piglet internal temperatures. The reading of six internal points in the cross-sectional section half, at 50 and 150 mm height, with 15 repetitions showed to a uniform distribution of the internal temperature in the lechonera ± 0,5 °C. The piglets room reaches 32 °C in 15 min of turning on, as a result of a thermostat.

Natural Movement of the Animal when Eating

Provide environmental, physical, social and thermal conditions cause welfare to the animal production units as a function of quality in the design of their housing. The designed feeders should allow easy access and respect for the natural movement of animals. In general, commercially available feeder management prioritizes and minimizes animal comfort. This analysis follows the natural movement of the animals head when feeding in conditions of freedom and indicates the space available in the feeding zone to be occupied by the feeders. With a conventional camera side and front make videos of animals when eaten in full freedom, we generate trajectories describing a sequential natural movement of the head of the animals. In the trajectories, we analyze the available spaces in the feeding area, identifying critical zones and create ideal feeding geometries. It offers food at different heights to determine where the animals prefer to eat. Finally, we compared with information of commercially available feeders. The species of animals are: pigs, cows, sheep, goats and turkeys. Feeders should be elliptical containers for food concentrate in one point, and the length of the major axis of the container is a function of range in the lateral head movement of animals. The length of the minor axis of the pot is depth. Further-more, the geometry obtained for the feeder of a cow shows the irrelevance of the perimeter railings in the feeding areas. We conclude that the existing commercial feeders do not create comfort and not encourage the natural movement.

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.