BAYESIAN REGULARIZERS OF ARTIFICIAL NEURAL NETWORKS APPLIED TO THE RELIABILITY FORECAST OF INTERNAL COMBUSTION MACHINES IN THE SHORT-TERM

Abstract

This work proposes a computational model to predict the Reliability Key Performance Indicator (KPI) to identify how available equipment will be in a time span of 22 days. The methodology to be used will be based on analyzes and tests of artificial neural network (ANN) architectures using the Bayesian Regularizers training algorithm, alternating the transfer functions in the hidden layers to find the best state of convergence and the minimum Root Mean Square Error (RMSE) value calculated between the real and simulated outputs. According to the results obtained by the training, validation and test steps, the algorithm presented a RMSE rate of 0.0000104202 and a 99.9% correlation between the real and simulated values, thus the model is able to identify which machine will have the greatest efficiency and less efficiency within the defined time span.