Data-Driven Predictive Maintenance: A Paper Making Case

Condition monitoring together with predictive maintenance of bearings and other equipment used by the industry avoids severe economic losses resulting from unexpected failures, greatly improves the system reliability and allows a more efficient usage of human experts’ time. This paper describes a predictive maintenance system, based on a data science approach. The system was developed and tested on a single real paper machine, and then verified with multiple external validations. Results show a proper behaviour of the approach on predicting different machine states with high accuracy.