Condition Monitoring

IMPROVE provides an innovative self-learning condition monitoring solution that prevents producers from unexpected breakdowns or product degradation. The outcome is translated into different software options, ready for industrial use.

Central characteristics of our data-driven condition monitoring:

  • Detecting and localising anomalies from learned normal behavior models
  • Providing different types of models for the anomaly detection and localisation
  • Easy implementation of additional types of models and monitoring algorithms as data acquisition for learning is flexible
  • Providing information about signals, last anomalies and also a live visualisation of the model
  • Allowing customers to forecast problems on the machine that could lead to production stoppages
  • Available as an additional after-sales service that provides regular reports of the machine ef¬ficiency throughout the lifetime
  • Carrying out short-time forecast analysis to identify wrong machine settings after a product changeover or problems related to raw material changes

IMPROVE’s condition monitoring software provides an allround solution that could lead to a great change in the service procedures of automatic machines and will significantly improve the production process.

For more information, please contact our coordinator Prof. Dr Oliver Niggemann.

You wish to receive information for special stakeholders? Please find the specific information linked below.

Alexander von Birgelen (HS-OWL) in the interview on the IMPROVE approach