PREDICTIVE MAINTENANCE POWERED BY MACHINE LEARNING AND SIMULATION

To optimize the balance between costs and reliability of cranes, it is important to perform maintenance when the risk of failures becomes high while possibly delaying planned maintenance when the crane shows no signs of possible problems. To accomplish this, we investigate the possibility of applying predictive maintenance for container-handling cranes. The application of predictive maintenance requires historical data collection and preprocessing of equipment sensor and maintenance data. To get a feeling of the possibilities and limitations of predictive maintenance for container-handling cranes, before investing time and money to collect operational data, we have used simulations to generate synthetic data for a few components of the cranes. Using the simulated crane data, a prediction model was trained to predict upcoming component failures. The results show that using simulation we can identify the possibilities and limitations of machine learning for predicting failures of components of the crane.