Continuous Maintenance System for Optimal Scheduling Based on Real-Time Machine Monitoring

Abstract

Manufacturing companies are seeking forms of maximizing profits, where reduction of maintenance costs plays a critical part. Avoiding unexpected breakdowns while maintaining productivity is possible through continuously monitoring machine performance, predicting when and where a failure will occur. This allows not only to reduce downtime but also to apply the best maintenance strategy and assure production targets. In this paper, a Continuous Maintenance System to achieve this is proposed. This system joins a Predictive Maintenance module with optimization and simulation modules. The Predictive Maintenance module makes use of a Gradient Boosting Classifier to predict which machine component will fail and schedule its maintenance. The optimization module uses a Genetic Algorithm to find the throughput values that reveal the best balance between production and degradation rates, and therefore, changing maintenance schedules according to production targets and machine degradation. Finally, a statistical simulation model based on real data distribution was used to examine effects of a certain throughput and maintenance schedule for each machine. Several classifiers were tested for the predictor, comparing their performance. Also, 3 different scenarios of a parallel production line were used to evaluate the proposed system.