Remaining Useful Life Estimation for Predictive Maintenance Using Feature Engineering

Abstract

Recently, machine learning techniques have been used to produce increasingly effective solutions to predict the remaining useful life (RUL) of assets accurately. This paper investigates the effect of different feature engineering approaches to the accuracy of RUL prediction. In this study, six different feature selection methods and many different regression algorithms were applied to choose the most accurate final model for prediction. Applied feature selection algorithms are Chi Squared, Spearman Correlation, Mutual Information, Fisher Score, Pearson Correlation and Count Based. Machine learning algorithms used in this work are Linear Regression, Bayesian Linear Regression, Poisson Regression, Neural Network Regression, Boosted Decision Tree Regression and Decision Forest Regression. In addition, two different feature engineering approaches were also tested on the benchmark dataset by transforming its feature space, with the goal of improving predictive modelling performance. Each combination of these methods were applied and totally 72 different models were constructed and compared with each other to evaluate their performances in terms of five different metrics, including mean absolute error, root mean squared error, relative absolute error, relative squared error and coefficient of determination.