A neural network approach for machine breakdown repair time

Abstract

Research on neural network applications have been carried out very extensively in recent days. The current trends in manufacturing sectors for solving their business operational problems have been very difficult and subjective. Many organizations have used various methods to solve machine breakdown's repair time, either reducing the time taken to repair or eliminate the particular occurrence. The traditional way for solving these machine breakdown issues was to predict the machine breakdown occurrence through preventive maintenance. Hence, in the present study, a neural network method was proposed to optimize the mean repair time for machine breakdown with regression models were evaluated from the trained neurons. The neurons were represented by the samples of repair time of previous years' record of a single machine. The results shows that the set of samples of repair time have critically influenced the optimized mean repair time for the machine. Various methodologies were used by comparing several grouped machine breakdown phenomena which showed more accurate regressions. The use of neural network, in the end of the study, gives significant changes in predicting machine breakdown repair time for the future years.