Development of a methodology for calculating reliability in one of the process areas of the vehicle assembly company called Ciauto Cía. Ltd.
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Abstract
The reliability analysis of critical systems in the industrial sector is a very useful tool to improve decision making in the maintenance department. Generally, traditional reliability analysis methods assume restorations of the equipment to its original condition, but in practice this does not happen, since interventions are generally carried out to correct only the failure that occurs at that moment; For this reason, the objective of this research was to develop a methodology to know the current reliability of repairable assets where minimal repairs are carried out, and its prediction for 5 years, with the calculation of the intensity of failures and the average time between failures. The sample was selected from the failure history records from January 2022 to May 2024 of the welding plant of a vehicle assembler, a Jack Knife diagram was made to prioritize the analysis of the systems that cause the most productive stops per repair have generated. A trend test was carried out to determine the bias that the historical data have and thus be able to adjust them to non-homogeneous Poisson stochastic processes, the Crow Amsaa and Log-linear model was used to select the one that best fits the data and is capable of generating forecasts with the lowest possible error. From the study carried out, it was determined that the systems that have caused the most productive stops are the SP-43 and SP-16 welding machines, and the MB-10 JIG. For the SP-43 system, the model that generated the lowest error for a forecast within 5 years was Crow Amsaa with an estimate of 48 failures and one failure every 233 work hours, while for the SP-16 and JIG MB systems -10, the log-linear model presented the best fit, predicting 19 failures, one failure every 987 hours and 22 failures, one every 822 hours of operation respectively.
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