Predictive Maintenance for Manufacturing

Industrial manufacturing relies on fully functional production machines that run under optimal conditions. Intelligent planning of the maintenance work on these machines is important in order to reduce the number and duration of production downtimes. Machine learning of models that predict maintenance is essential to reduce the risk of unwanted and unplanned production downtime.

Downtime of manufacturing machines
Few outlier data for AI training

Predictive Maintenance

Developing a high-precision machine learning model for predictive maintenance depends on the data available. The number of machine failures is low and the occurrence of important events in the training data is rare. Therefore, it is difficult to build a model with high precision and possible future machine repair needs go undetected until a machine outage occurs.

Fleet of Industrial Machines

Federated learning can be used to train on a fleet of industrial machines in various production companies in order to expand knowledge about machine failures, repairs, or maintenance. This technology enables full data protection and individual machine information is treated confidentially in order to avoid conclusions about the production process. The production process is improved by predicting the necessary maintenance work, which allows the machines to operate under optimal working conditions. In addition, new production machines benefit from the existing knowledge of other companies and machines.

Fleet of industrial machines

Advantages

Data Privacy
More Cases to Learn From
Better Predictions
No Cold-Start Issue

Working with Experts

Logo of KOSMoS project

The KOSMoS project is a consortium of several companies working together to ensure knowledge sharing without sharing internal production data. Different state-of-art technologies as blockchain, machine learning models for predictive maintenance and federated learning are brought together by Inovex.