The cooling system has a fundamental role in the die casting process because the cooling system will directly affect the quality of the casting results. However, to ensure the cooling system works well from time to time is not easy. More frequent and regularly checking the cooling system is one possible way to ensure it. Nevertheless, this way is disrupting the casting process. In this paper, a predictive maintenance technique is proposed with autonomous analysis based on machine learning and sensor data. A SCADA system collects real-time data from several sensor combinations. This data is then passed to a machine learning algorithm for predicting cooling system conditions, i.e., predicting future system failure. The proposed predictive maintenance system is expected to be able to predict the damage better. Therefore, it will reduce the possibility of unplanned system failure. Also, it is increasing the maintenance process. The maintenance is carried out according to the cooling system conditions and without the need periodically check the cooling system.

@inproceedings{oscm-2020-1790,
  title={PREDICTIVE MAINTENANCE OF COOLING SYSTEM WITH SENSOR COMBINATION AND SCADA},
  year={2020},
  author={Engelbert Harsandi Erik Suryadarma and The Jin Ai},
  booktitle={Proceedings of the 10th International Conference on Operations and Supply Chain Management, 2020},
  doi={10.31387/oscm490258}
}

Engelbert Harsandi Erik Suryadarma, The Jin Ai (2020). PREDICTIVE MAINTENANCE OF COOLING SYSTEM WITH SENSOR COMBINATION AND SCADA. In Proceedings of the 10th International Conference on Operations and Supply Chain Management, 2020. https://doi.org/10.31387/oscm490258