Machine Learning and Optimization for Predictive Maintenance based on Predicting Failure in the Next Five Days

Abstract

This study proposes a framework to predict machine failures using sensor data and optimize predictive/corrective maintenance schedule. Using historical data, machine learning (ML) models are trained to predict the failure probabilities for the next five days. Multiple algorithms, including feature extraction techniques, selections, and ML models (both regression and classification based) are compared. The machine learning models’ output is fed to an optimization model to propose an optimized maintenance policy, and we demonstrate how prediction models can help increase system reliability at lower costs.