A comparison of state-of-the-art machine learning algorithms on fault indication and remaining useful life determination by telemetry data

Abstract

Contemporary trends in the diffusion of artificial intelligence technologies has increased the number of studies on predictive maintenance, a recent focus of interest in many industrial domains. Despite the increased interest in the use of machine learning for predictive maintenance, few studies involve thorough comparisons of machine learning algorithms' performance on predictive maintenance applications. This work aims to predict the remaining useful life and machine failures and compares five different algorithms: Random Forest, Gradient Boosted Tree, K-Nearest Neighbors, Multilayer Perceptron and LightGBM. Our results suggest better performances for binary classification using Random Forest, and for regression using LightGRM comnared to other selected algorithms.