Ensemble Methodology for Automated Machine Predictive Maintenance Classification

Machine predictive maintenance is important for low-cost maintenance of manufacturing machines. Current methodologies use different classification models for predicting machine maintenance and failures. We propose an ensemble model that utilizes current state-of-the-art classification models for predictive maintenance: random forest, sequential neural network, X G Boost, decision tree, and support vector machine models.