Application of a Selected Pseudorandom Number Generator for the Reliability of Farm Tractors

Knowledge of the use-to-failure periods of process equipment, including agricultural vehicles, is essential for the determination of their durability and reliability. Obtaining any empirical data on this issue is difficult and sometimes impossible. Experimental studies are costly and time-consuming. Manufacturers are usually reluctant to share such data, claiming that the information is classified for the sake of their companies. The purpose of this study was to compare empirical data with data generated using adequate statistical tools. The newly generated and very similar in value pseudorandom numbers were obtained by simulations using the Monte Carlo, Latin hypercube sampling and Iman-Conover methods. Reliability function graphs obtained from the generated time-series (use-to-failure periods) with matching Weibull distribution had very similar shape and scale parameters. They were are also comparable to parameters from experimental data extracted from a Polish Zetor agricultural tractor service station. The validation of the applied methods was limited as it was carried out only on the basis of the available data. Analysis of line graphs of cumulative deviations of the values of use-to-failure periods (times-to-fail) generated against empirical times-to-fail indicated that the best method in the studied case was the Monte Carlo method.

Automated summary

Knowledge of the use-to-failure periods of process equipment, including agricultural vehicles, is crucial for assessing their durability and reliability. However, obtaining empirical data on this issue is challenging. This study compares empirical data with data generated using statistical tools and finds that the Monte Carlo method is the most effective in the studied case. The findings validate the use of these methods in predicting the use-to-failure periods.