Abstract

This study aims to compare the stochastic process model designed as a nonhomogeneous Poisson process and α-series process, to obtain a better process for using monotonous trend data. The α-series process is a stochastic process with a monotone trend, while the NHPP is a general process of the ordinary Poisson process and it is used as a model for a series of events that occur randomly over a variable period of time. Data on the daily fault time of machines in Bahrri Thermal Station in Sudan was analyzed during the interval from first January 2021, to July 31, 2021, to acquire the best
stochastic process model used to analyze monotone trend data. The results revealed that the NHPP model could be the most suitable process model for the description of the daily fault time of machines in Bahrri Thermal Station according to lowest MSE, RMSE, Bias, MPE, and highest. The current study concluded that through the NHPP, the fault time of machines and repair rate occur in an inconsistent way. The further value of this study is that it compared NHPP and α-series to obtain a better process for using monotone trend data and prediction. Meanwhile, the other studies in this field focused on comparing methods of estimation parameters of the NHPP and the αseries process. The distinctive scientific addition of this study stems from displaying the precision of the NHPP better than the α-series process in the
case of monotone trend data.